Journal articles on the topic 'Image registration method'
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Bingjian, Wang, Lu Quan, Li Yapeng, Li Fan, Bai Liping, Lu Gang, and Lai Rui. "Image registration method for multimodal images." Applied Optics 50, no. 13 (April 25, 2011): 1861. http://dx.doi.org/10.1364/ao.50.001861.
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Xu, Hong Kui, Ming Yan Jiang, and Ming Qiang Yang. "An Image Registration Method Combing Feature Constraint with Multilevel Strategy." Applied Mechanics and Materials 58-60 (June 2011): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.286.
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A novel method combing feature constraint with multilevel strategy to improve simultaneously the registration accuracy and speed is proposed for non-parametric image registrations. To images between which the local difference is large, integrating feature constraint constructed with local structure information of images into objective function of image registration improves the registration accuracy. When applying feature constraint under multilevel strategy, parameter searching is prevented from entrapped into local extremum by using the optimization result on coarser levels as the starting points on finer levels; meanwhile traditional optimization methods without demanding intelligent optimization algorithms which consume more time can find the accurate registration parameter on finer levels, so registration speed is improved. Experimental results indicate that this method can finish fast and accurate registration for images between which there exists large local difference.
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Mezura-Montes, Efrén, Héctor-Gabriel Acosta-Mesa, Darío-del-Sinaí Ramírez-Garcés, Nicandro Cruz-Ramírez, and Rodolfo Hernández-Jiménez. "An Image Registration Method for Colposcopic Images." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/285962.
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A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.
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Yang, Lu Jing, Wei Hao, and Chong Lun Li. "A Modified Phase Correlation Method for Image Registration." Applied Mechanics and Materials 48-49 (February 2011): 48–51. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.48.
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Image registration is a very fundamental and important part in many multi-sensor image based applications. Phase correlation-based image registration method is widely concerned for its small computation amount, strong anti-interference property. However, it can only solve the image registration problem with translational motion. Hence, we proposed a modified phase correlation registration method in the paper. We analyzed the principle of registration, gave the flow chart, and applied the method to the SAR image registration problems with scaling, rotation and translation transformation. Simulation results show that the method can accurately estimate the translation parameters, zoom scale and rotation angle of registrating image relative to the reference image.
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Fan, Shu Kai S., Yu Chiang Chuang, and Jia Rong Wu. "A New Cross-Correlation Based Image Registration Method." Applied Mechanics and Materials 58-60 (June 2011): 1979–84. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1979.
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Image registration is a fundamental task for combining two or more images taken from different viewpoints, different times, or different sensors. It is a process of determining the point by point correspondence between two images from the same scene. The proposed image registration method uses the area-based approach to process image registration and the objective is to find the maximum similarity through the cross-correlation measure. Most cross-correlation methods are developed based on image intensities for the direct matching purpose. However, it is extremely sensitive to the intensity changes. To counteract illumination effect, the proposed method replaces the intensity with the gradient information, and this concept comes originally from the Hough transform that points having the same parameters and should be on the same line. These two parameters are combined as the similarity between images for image registration. The experimental results obtained by means of several test images illustrate the effectiveness of the proposed image registration method.
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Liang, Bo, Xi Chen, Lan Yu, Song Feng, Yangfan Guo, Wenda Cao, Wei Dai, Yunfei Yang, and Ding Yuan. "High-precision Multichannel Solar Image Registration Using Image Intensity." Astrophysical Journal Supplement Series 261, no. 2 (July 20, 2022): 10. http://dx.doi.org/10.3847/1538-4365/ac7232.
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Abstract Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration.
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Zheng, Qian, Qiang Wang, Xiaojuan Ba, Shan Liu, Jiaofen Nan, and Shizheng Zhang. "A Medical Image Registration Method Based on Progressive Images." Computational and Mathematical Methods in Medicine 2021 (July 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/4504306.
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Background. Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods. As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results. For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions. The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.
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Nobnop, Wannapha, Imjai Chitapanarux, Somsak Wanwilairat, Ekkasit Tharavichitkul, Vicharn Lorvidhaya, and Patumrat Sripan. "Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882118. http://dx.doi.org/10.1177/1533033818821186.
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Introduction: The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. Methods: The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses. Results: The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration. Conclusions: The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.
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Watcharawipha, Anirut, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul. "Space Independent Image Registration Using Curve-Based Method with Combination of Multiple Deformable Vector Fields." Symmetry 11, no. 10 (September 28, 2019): 1210. http://dx.doi.org/10.3390/sym11101210.
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This paper proposes a novel curve-based or edge-based image registration technique that utilizes the curve transformation function and Gaussian function. It enables deformable image registration between images in different spaces, e.g., different color spaces or different medical image modalities. In particular, piecewise polynomial fitting is used to fit a curve and convert it to the global cubic B-spline control points. The transformation between the curves in the reference and source images are performed by using these control points. The image area is segmented with respect to the reference curve for the moving pixels. The Gaussian function, which is symmetric about the coordinates of the points of the reference curve, was used to improve the continuity in the intra- and inter-segmented areas. The overall result on curve transformation by means of the Hausdroff distance was 5.820 ± 1.127 pixels on average on several 512 × 512 synthetic images. The proposed method was compared with an ImageJ plugin, namely bUnwarpJ, and a software suite for deformable image registration and adaptive radiotherapy research, namely DIRART, to evaluate the image registration performance. The experimental result shows that the proposed method yielded better image registration performance than its counterparts. On average, the proposed method could reduce the root mean square error from 2970.66 before registration to 1677.94 after registration and can increase the normalized cross-correlation coefficient from 91.87% before registration to 97.40% after registration.
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Zhou, Wu, and Yaoqin Xie. "Interactive Multigrid Refinement for Deformable Image Registration." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/532936.
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Deformable image registration is the spatial mapping of corresponding locations between images and can be used for important applications in radiotherapy. Although numerous methods have attempted to register deformable medical images automatically, such as salient-feature-based registration (SFBR), free-form deformation (FFD), and demons, no automatic method for registration is perfect, and no generic automatic algorithm has shown to work properly for clinical applications due to the fact that the deformation field is often complex and cannot be estimated well by current automatic deformable registration methods. This paper focuses on how to revise registration results interactively for deformable image registration. We can manually revise the transformed image locally in a hierarchical multigrid manner to make the transformed image register well with the reference image. The proposed method is based on multilevel B-spline to interactively revise the deformable transformation in the overlapping region between the reference image and the transformed image. The resulting deformation controls the shape of the transformed image and produces a nice registration or improves the registration results of other registration methods. Experimental results in clinical medical images for adaptive radiotherapy demonstrated the effectiveness of the proposed method.
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Nie, Xuan, Rongchun Zhao, and Zetao Jiang. "A new image registration method for grey images." Journal of Electronics (China) 21, no. 5 (September 2004): 426–31. http://dx.doi.org/10.1007/bf02687933.
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Cui, Kunpeng, Panpan Fu, Yinghao Li, and Yusong Lin. "Bayesian Fully Convolutional Networks for Brain Image Registration." Journal of Healthcare Engineering 2021 (July 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/5528160.
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The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.
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Yang, Huan, Pengjiang Qian, and Chao Fan. "An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis." Computational and Mathematical Methods in Medicine 2020 (June 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/2684851.
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Multimodal registration is a challenging task due to the significant variations exhibited from images of different modalities. CT and MRI are two of the most commonly used medical images in clinical diagnosis, since MRI with multicontrast images, together with CT, can provide complementary auxiliary information. The deformable image registration between MRI and CT is essential to analyze the relationships among different modality images. Here, we proposed an indirect multimodal image registration method, i.e., sCT-guided multimodal image registration and problematic image completion method. In addition, we also designed a deep learning-based generative network, Conditional Auto-Encoder Generative Adversarial Network, called CAE-GAN, combining the idea of VAE and GAN under a conditional process to tackle the problem of synthetic CT (sCT) synthesis. Our main contributions in this work can be summarized into three aspects: (1) We designed a new generative network called CAE-GAN, which incorporates the advantages of two popular image synthesis methods, i.e., VAE and GAN, and produced high-quality synthetic images with limited training data. (2) We utilized the sCT generated from multicontrast MRI as an intermediary to transform multimodal MRI-CT registration into monomodal sCT-CT registration, which greatly reduces the registration difficulty. (3) Using normal CT as guidance and reference, we repaired the abnormal MRI while registering the MRI to the normal CT.
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Zhu, Huabo, Xu Han, and Yourui Tao. "Efficient stitching method of tiled scanned microelectronic images." Measurement Science and Technology 33, no. 7 (April 15, 2022): 075404. http://dx.doi.org/10.1088/1361-6501/ac632a.
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Abstract Sparse features and repetitive textures are frequently presented in microelectronic microscopic images. Therefore, it is challenging for image stitching to meet the requirements of high-speed precision manufacturing. A novel image stitching method for tiled images is proposed to generate panoramic images of microelectronics quickly and accurately. According to the preset scan trajectory, grids were established between adjacent images for feature matching. The clustering algorithm was used to screen reasonable and multiple sets of registrations. Then, all registrations were used as connecting edges, and images were used as nodes, to create a multigraph. The unique registration in multigraph was solved by a non-linear minimization problem with linear constraints. Finally, image transformations were computed in global optimization for rendering panoramic images via image warping. The experimental results show that the proposed method improves the stability and efficiency of image stitching, furthermore, it maintains an equivalent level of precision as the Fiji and microscopy image stitching tool methods.
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Hu, Wei Wei. "A New Method Based on Image Registration Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 3305–8. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3305.
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We introduce a new algorithm for Image Registration and Stitching. The algorithm is designed to be extremely efficient and fast in its execution and is intended for use in stitching images extracted from a video stream of a camera. This algorithm is not universally applicable to all the image registration and stitching problems. It is customized to be used to generate single images of surfaces such as a conveyor belt or undercarriage of vehicles, which cannot be captured by a single photo. The algorithm works by extracting edges of the two images to be registered. Then it selects a reference section from the first image and search in the second image where it finds the best match for that section. The best match is the east difference score. We present full details of how the extraction of the heuristic is done from the inputs and how it drastically reduces the execution time of the algorithm. The paper also contains a full section on comparing our algorithm with a set of existing algorithms. Our algorithm outperforms the existing ones for all the common image sizes.
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GILLAN, STEVEN, and PANAJOTIS AGATHOKLIS. "A METHOD FOR FACE RECOGNITION USING IMAGE REGISTRATION." Journal of Circuits, Systems and Computers 20, no. 07 (November 2011): 1419–39. http://dx.doi.org/10.1142/s0218126611007955.
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This paper presents a technique for face recognition that is based on image registration. The face recognition technique consists of three parts: a training part, an image registration part and a post-processing part. The image registration technique is based on finding a set of feature points in the two images and using these feature points for registration. This is done in four steps. In the first, images are filtered with the Mexican-hat wavelet to obtain the feature point locations. In the second, the Zernike moments of neighborhoods around the feature points are calculated and compared in the third step to establish correspondence between feature points in the two images. In the fourth, the transformation parameters between images are obtained using an iterative least squares technique to eliminate outliers.1,2 During training, a set of images are chosen as the training images and the Zernike moments for the feature points of the training images are obtained and stored. The choice of training images depends on the changes of poses and illumination that are expected. In the registration part, the transformation parameters to register the training images with the images under consideration are obtained. In the post-processing, these transformation parameters are used to determine whether a valid match is found or not. The performance of the proposed method is evaluated using various face databases3–5 and it is compared with the performance of existing techniques. Results indicate that the proposed technique gives excellent results for face recognition in conditions of varying pose, illumination, background and scale.
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Chuang, Yu Chiang, and Shu Kai S. Fan. "An Image Registration Method Based upon Information Theorem on Overlapped Region." Applied Mechanics and Materials 58-60 (June 2011): 1985–89. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1985.
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Digital image and video have been widely applied to many practical applications due to their simple image acquirement. Image registration is an important image processing for integrating information from images. For Image registration, it is intuitive to orientate images by matching corresponding pixels being considered idealistically identical on the overlapping region. Based on this idea, this article proposes an image registration method that applies the information theorem to the corresponding intensity data. An entropy-based objective function is developed upon the histogram of the intensity differences as to evaluate the similarity between images. Intensity differences represent the differences of the corresponding pixels between the referenced and sensed images on the overlapped region. The sensed image is aligned to the referenced image by minimizing the proposed objective function through iteratively updating the parameters of the projective transformation during the optimization process. The experimental results obtained by means of several test image sets illustrate the effectiveness and feasibility of the proposed image registration method.
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Zhao, Xin, Hui Li, Ping Wang, and Linhai Jing. "An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images." Remote Sensing 13, no. 17 (August 29, 2021): 3425. http://dx.doi.org/10.3390/rs13173425.
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Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.
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Tang, Jun. "Image Registration Using Clustering Algorithm." Advanced Materials Research 108-111 (May 2010): 63–68. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.63.
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This paper proposed a new method of image registration based on clustering algorithm. It used clustering algorithm to cluster all the feature vectors of images, and adopted EM algorithm to optimize the parameters and algorithm. Experimental result shows that the proposed image registration method can improve the precise of image registration, and reduce error.
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Zhao, Xin, Hui Li, Ping Wang, and Linhai Jing. "An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment." Sensors 20, no. 8 (April 17, 2020): 2286. http://dx.doi.org/10.3390/s20082286.
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For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy.
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Lu, Jiahao, Johan Öfverstedt, Joakim Lindblad, and Nataša Sladoje. "Is image-to-image translation the panacea for multimodal image registration? A comparative study." PLOS ONE 17, no. 11 (November 28, 2022): e0276196. http://dx.doi.org/10.1371/journal.pone.0276196.
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Despite current advancement in the field of biomedical image processing, propelled by the deep learning revolution, multimodal image registration, due to its several challenges, is still often performed manually by specialists. The recent success of image-to-image (I2I) translation in computer vision applications and its growing use in biomedical areas provide a tempting possibility of transforming the multimodal registration problem into a, potentially easier, monomodal one. We conduct an empirical study of the applicability of modern I2I translation methods for the task of rigid registration of multimodal biomedical and medical 2D and 3D images. We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration. We evaluate these method combinations on four publicly available multimodal (2D and 3D) datasets and compare with the performance of registration achieved by several well-known approaches acting directly on multimodal image data. Our results suggest that, although I2I translation may be helpful when the modalities to register are clearly correlated, registration of modalities which express distinctly different properties of the sample are not well handled by the I2I translation approach. The evaluated representation learning method, which aims to find abstract image-like representations of the information shared between the modalities, manages better, and so does the Mutual Information maximisation approach, acting directly on the original multimodal images. We share our complete experimental setup as open-source (https://github.com/MIDA-group/MultiRegEval), including method implementations, evaluation code, and all datasets, for further reproducing and benchmarking.
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Chen, Yu, Dongxiang Lu, and Guy Courbebaisse. "A Parallel Image Registration Algorithm Based on a Lattice Boltzmann Model." Information 11, no. 1 (December 19, 2019): 1. http://dx.doi.org/10.3390/info11010001.
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Image registration is a key pre-procedure for high level image processing. However, taking into consideration the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. To speed up the registration algorithm, parallel computation is a relevant strategy. Parallelizing the algorithm by implementing Lattice Boltzmann method (LBM) seems a good candidate. In consequence, this paper proposes a novel parallel LBM based model (LB model) for image registration. The main idea of our method consists in simulating the convection diffusion equation through a LB model with an ad hoc collision term. By applying our method on computed tomography angiography images (CTA images), Magnet Resonance images (MR images), natural scene image and artificial images, our model proves to be faster than classical methods and achieves accurate registration. In the continuity of 2D image registration model, the LB model is extended to 3D volume registration providing excellent results in domain such as medical imaging. Our method can run on massively parallel architectures, ranging from embedded field programmable gate arrays (FPGAs) and digital signal processors (DSPs) up to graphics processing units (GPUs).
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ZHOU, HONG, and RAY SEYFARTH. "SEMI AUTOMATIC REGISTRATION OF PARTIALLY OVERLAPPED AERIAL IMAGES VIA PATTERN SEARCH METHOD." International Journal of Image and Graphics 06, no. 03 (July 2006): 393–405. http://dx.doi.org/10.1142/s0219467806002306.
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This paper presents a semi-automatic registration algorithm for partially overlapped aerial images which has been successfully tested with images of the Mississippi Delta area. In this algorithm, each individual aerial image is registered by employing a pattern search method. This pattern search method considers an affine transformation without shear as a 5-parameter vector and searches toward the gradient direction that results in higher similarity values between the reference and the sensed images. The registration of the first aerial image requires some initial manual work. After the registration of the first image, based on the overlap of the neighboring images and the existing transformation parameters for the first image, the search starting point of the second image can be automatically obtained for the registration of the second image. This process can be repeated for the remaining aerial images in a sequential order. Experimental results with 12 sample images demonstrate the success of this algorithm.
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Li, Wan Bing, Hong Wei Quan, and Xia Fei Huang. "Feature Extraction Method Based on Moment Invariants." Advanced Materials Research 936 (June 2014): 2263–66. http://dx.doi.org/10.4028/www.scientific.net/amr.936.2263.
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To match two or more images originated from the same scenario, a new fast automatic registration algorithm based on sparse feature point extraction is proposed. At the first step, the improved Harris corner detection algorithm is used to get two sets of feature points from the reference image and registration image. Second, a group of sparse feature points are selected from the reference image set as initial control points. Then, the corresponding matching points in the registration image set are searched based on local moment invariant similarity detection. Experimental results demonstrate that this method is fast and efficient.
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Haber, Eldad, and Jan Modersitzki. "A Multilevel Method for Image Registration." SIAM Journal on Scientific Computing 27, no. 5 (January 2006): 1594–607. http://dx.doi.org/10.1137/040608106.
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Guarneri, I., M. Guarnera, G. Lupica, and S. Casale. "Image registration method for consumer devices." IEEE Transactions on Consumer Electronics 51, no. 3 (August 2005): 1014–19. http://dx.doi.org/10.1109/tce.2005.1510516.
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Guryanov, F. A., and A. S. Krylov. "Optimization Method for Cell Image Registration." Programming and Computer Software 44, no. 4 (July 2018): 266–70. http://dx.doi.org/10.1134/s0361768818040072.
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Flusser, Jan. "An adaptive method for image registration." Pattern Recognition 25, no. 1 (January 1992): 45–54. http://dx.doi.org/10.1016/0031-3203(92)90005-4.
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Zhu, Ning, Mohammad Najafi, Bin Han, Steven Hancock, and Dimitre Hristov. "Feasibility of Image Registration for Ultrasound-Guided Prostate Radiotherapy Based on Similarity Measurement by a Convolutional Neural Network." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882196. http://dx.doi.org/10.1177/1533033818821964.
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Purpose: Registration of 3-dimensional ultrasound images poses a challenge for ultrasound-guided radiation therapy of the prostate since ultrasound image content changes significantly with anatomic motion and ultrasound probe position. The purpose of this work is to investigate the feasibility of using a pretrained deep convolutional neural network for similarity measurement in image registration of 3-dimensional transperineal ultrasound prostate images. Methods: We propose convolutional neural network-based registration that maximizes a similarity score between 2 identical in size 3-dimensional regions of interest: one encompassing the prostate within a simulation (reference) 3-dimensional ultrasound image and another that sweeps different spatial locations around the expected prostate position within a pretreatment 3-dimensional ultrasound image. The similarity score is calculated by (1) extracting pairs of corresponding 2-dimensional slices (patches) from the regions of interest, (2) providing these pairs as an input to a pretrained convolutional neural network which assigns a similarity score to each pair, and (3) calculating an overall similarity by summing all pairwise scores. The convolutional neural network method was evaluated against ground truth registrations determined by matching implanted fiducial markers visualized in a pretreatment orthogonal pair of x-ray images. The convolutional neural network method was further compared to manual registration and a standard commonly used intensity-based automatic registration approach based on advanced normalized correlation. Results: For 83 image pairs from 5 patients, convolutional neural network registration errors were smaller than 5 mm in 81% of the cases. In comparison, manual registration errors were smaller than 5 mm in 61% of the cases and advanced normalized correlation registration errors were smaller than 5 mm only in 25% of the cases. Conclusion: Convolutional neural network evaluation against manual registration and an advanced normalized correlation -based registration demonstrated better accuracy and reliability of the convolutional neural network. This suggests that with training on a large data set of transperineal ultrasound prostate images, the convolutional neural network method has potential for robust ultrasound-to-ultrasound registration.
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Lu, W., X. Yue, Y. Zhao, and C. Han. "A SAR IMAGE REGISTRATION METHOD BASED ON SIFT ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 623–27. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-623-2017.
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In order to improve the stability and rapidity of synthetic aperture radar (SAR) images matching, an effective method was presented. Firstly, the adaptive smoothing filtering was employed for image denoising in image processing based on Wallis filtering to avoid the follow-up noise is amplified. Secondly, feature points were extracted by a simplified SIFT algorithm. Finally, the exact matching of the images was achieved with these points. Compared with the existing methods, it not only maintains the richness of features, but a-lso reduces the noise of the image. The simulation results show that the proposed algorithm can achieve better matching effect.
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Pan, Meisen, Jianjun Jiang, Fen Zhang, and Qiusheng Rong. "MEDICAL IMAGE REGISTRATION BASED ON IMPROVED FUZZY C-MEANS CLUSTERING." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (August 2015): 1550032. http://dx.doi.org/10.4015/s1016237215500325.
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The mutual information (MI) technology and the iterative closest point (ICP) algorithm, as intensity-based and feature-based image registration methods respectively, are commonly put into use in medical image registration. But some naturally existing things which restrict the further development need to be faced and be solved. On one hand, they remain heavy calculation costs and low registration efficiencies. On the other hand, since they seriously depend on whether the initial rotation and translation registration parameters can be exactly selected, they often trap in the local optima and even fail to register images. In this paper, we compute the centroids of the reference and floating images by using the image moments to obtain the initial translation values, and use improved fuzzy C-means clustering (IFCM) to classify the image coordinates. Before clustering, this proposed method first centralizes the medical image coordinates, creates the two-row coordinate matrix to construct the two-dimensional (2D) sample set partitioned into two classes, and computes the slope of a straight line fitted to the two classes, finally derives the rotation angle from solving the arc tangent of the slope and obtains the initial rotation values. The experimental results show that, this proposed method has a fairly simple implementation, a low computational load, a fast registration and good registration accuracy. Also, it can efficiently avoid trapping in the local optima and meets both mono-modality and multi-modality image registrations.
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Hernandez-Matas, Carlos, Xenophon Zabulis, Areti Triantafyllou, Panagiota Anyfanti, Stella Douma, and Antonis A. Argyros. "FIRE: Fundus Image Registration dataset." Modeling and Artificial Intelligence in Ophthalmology 1, no. 4 (July 7, 2017): 16–28. http://dx.doi.org/10.35119/maio.v1i4.42.
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Purpose: Retinal image registration is a useful tool for medical professionals. However, performance evaluation of registration methods has not been consistently assessed in the literature. To address that, a dataset comprised of retinal image pairs annotated with ground truth and an evaluation protocol for registration methods is proposed.Methods: The dataset is comprised by 134 retinal fundus image pairs. These pairs are classified into three categories, according to characteristics that are relevant to indicative registration applications. Such characteristics are the degree of overlap between images and the presence/absence of anatomical differences. Ground truth in the form of corresponding image points and a protocol to evaluate registration performance are provided.Results: The proposed protocol is shown to enable quantitative and comparative evaluation of retinal registration methods under a variety of conditions.Conclusion: This work enables the fair comparison of retinal registration methods. It also helps researchers to select the registration method that is most appropriate given a specific target use.
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Wu, Shu Guang, Shu He, and Xia Yang. "The Application of SIFT Method towards Image Registration." Advanced Materials Research 1044-1045 (October 2014): 1392–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1392.
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The scale invariant features transform (SIFT) is commonly used in object recognition,According to the problems of large memory consumption and low computation speed in SIFT (Scale Invariant Feature Transform) algorithm.During the image registration methods based on point features,SIFT point feature is invariant to image scale and rotation, and provides robust matching across a substantial range of affine distortion. Experiments show that on the premise that registration accuracy is stable, the proposed algorithm solves the problem of high requirement of memory and the efficiency is improved greatly, which is applicable for registering remote sensing images of large areas.
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Li, Shengping, Jie Zhang, Gaofei Liu, Nanhui Chen, Lulu Tian, Libing Bai, and Cong Chen. "Image Registration for Visualizing Magnetic Flux Leakage Testing under Different Orientations of Magnetization." Entropy 25, no. 1 (January 13, 2023): 167. http://dx.doi.org/10.3390/e25010167.
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The Magnetic Flux Leakage (MFL) visualization technique is widely used in the surface defect inspection of ferromagnetic materials. However, the information of the images detected through the MFL method is incomplete when the defect (especially for the cracks) is complex, and some information would be lost when magnetized unidirectionally. Then, the multidirectional magnetization method is proposed to fuse the images detected under different magnetization orientations. It causes a critical problem: the existing image registration methods cannot be applied to align the images because the images are different when detected under different magnetization orientations. This study presents a novel image registration method for MFL visualization to solve this problem. In order to evaluate the registration, and to fuse the information detected in different directions, the mutual information between the reference image and the MFL image calculated by the forward model is designed as a measure. Furthermore, Particle Swarm Optimization (PSO) is used to optimize the registration process. The comparative experimental results demonstrate that this method has a higher registration accuracy for the MFL images of complex cracks than the existing methods.
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Han, Dian Yuan, and Xin Yuan Huang. "Image Registration of Blown and Swayed Trees." Key Engineering Materials 474-476 (April 2011): 2183–88. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.2183.
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This paper concerns the problem of blown and swayed tree image registration. The swayed tree leaves and little branches contain plenty of details or feature points, but matching with these points may result in error image registration. In this work, we delete some details of leaves and little and thin branches by using of morphological image processing methods, then extract the relatively invariant crotch features of trees from thinning images, lastly we adjusted the matching points with L-M algorithm. Results show our method is insensitive to the ordering, rotation and scale of the input images so it can be used in image stitching and retrieval of images & videos.
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Tomaževič, Dejan, Boštjan Likar, and Franjo Pernuš. "MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION." Image Analysis & Stereology 31, no. 1 (March 15, 2012): 43. http://dx.doi.org/10.5566/ias.v31.p43-53.
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Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.
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Li, Kai, Yongsheng Zhang, Zhenchao Zhang, and Guangling Lai. "A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences." Remote Sensing 11, no. 4 (February 25, 2019): 470. http://dx.doi.org/10.3390/rs11040470.
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Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.
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Zhou, Zibo, Libing Jiang, and Zhuang Wang. "A novel image registration method for InISAR 3D imaging." MATEC Web of Conferences 232 (2018): 02044. http://dx.doi.org/10.1051/matecconf/201823202044.
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Image registration is a key intermediate step for Interferometric Inverse Synthetic Aperture Radar (InISAR) three-dimensional (3D) imaging. It arranges the same scatterers of the target on the same pixel cell in different ISAR images, which makes the interferometric processing carried on between the same scatterers to obtain its 3D coordinates. This paper proposes a novel ISAR image registration method of three steps. Firstly, chirp Fourier transform is used to estimate the rotational angular velocity of the target. Secondly, the compensation phase is constructed, according to the rotational angular velocity, to eliminate the wave path difference between different radars echoes. Finally, two-dimensional (2D) Fourier transform is used to yield registered ISAR images. The proposed method achieves the ISAR image registration through phase compensation in echo field, therefore, no extra computation is needed in image field. The experiment results demonstrate the advantages of the proposed method in precision, computation efficiency and practicability.
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Li, Donghui, and Monan Wang. "A 3D Image Registration Method for Laparoscopic Liver Surgery Navigation." Electronics 11, no. 11 (May 24, 2022): 1670. http://dx.doi.org/10.3390/electronics11111670.
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At present, laparoscopic augmented reality (AR) navigation has been applied to minimally invasive abdominal surgery, which can help doctors to see the location of blood vessels and tumors in organs, so as to perform precise surgery operations. Image registration is the process of optimally mapping one or more images to the target image, and it is also the core of laparoscopic AR navigation. The key is how to shorten the registration time and optimize the registration accuracy. We have studied the three-dimensional (3D) image registration technology in laparoscopic liver surgery navigation and proposed a new registration method combining rough registration and fine registration. First, the adaptive fireworks algorithm (AFWA) is applied to rough registration, and then the optimized iterative closest point (ICP) algorithm is applied to fine registration. We proposed a method that is validated by the computed tomography (CT) dataset 3D-IRCADb-01. Experimental results show that our method is superior to other registration methods based on stochastic optimization algorithms in terms of registration time and accuracy.
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Zhong, Aiqi, Qiang Fu, Danfei Huang, Kang Zong, and Huilin Jiang. "A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging." Applied Sciences 12, no. 24 (December 8, 2022): 12596. http://dx.doi.org/10.3390/app122412596.
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In multi-source camera collaborative imaging research, it is known that the differences in size and resolution of the sensor chip, the angle of view and field of view when imaging, and the imaging characteristics of optical systems between cameras, makes image registration a topic that can never be avoided in data analysis and post-processing. Additionally, lacking common features between multi-source images means that the accurate registration of multi-modal images can only be completed manually. Aiming at the registration problem of the polarization parameter image and infrared image, this study takes advantage of the invariant feature of the imaging target topology and introduces the image texture-based segmentation method to obtain the target topology structure. Subsequently, the registration control points are extracted based on the target topology skeleton, which can break through the limitation of feature differences, improve the robustness of the algorithm to target transformation, and realize the automatic registration of multi-source images.
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Yan, Hui, Ren Lei, Jackie Wu, Fu Di, and Fang-Fang Yin. "Evaluation of Image Enhancement Method on Target Registration Using Cone Beam CT in Radiation Therapy." Clinical medicine. Oncology 2 (January 2008): CMO.S512. http://dx.doi.org/10.4137/cmo.s512.
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An intensity based six-degree image registration algorithm between cone-beam CT (CBCT) and planning CT has been developed for image-guided radiation therapy (IGRT). CT images of an anthropomorphic chest phantom were acquired using conventional CT scanner and corresponding CBCT was reconstructed based on projection images acquired by an on-board imager (OBI). Both sets of images were initially registered to each other using attached fudicial markers to achieve a golden standard registration. Starting from this point, an offset was applied to one set of images, and the matching result was found by a gray-value based registration method. Finally, The registration error was evaluated by comparing the detected shifts with the known shift. Three window-level (WL) combinations commonly used for image enhancement were examined to investigate the effect of anatomical information of Bony only (B), Bone+Tissue (BT), and Bone+Tissue+Air (BTA) on the accuracy and robustness of gray-value based registration algorithm. Extensive tests were performed in searching for the attraction range of registration algorithm. The widest attraction range was achieved with the WL combination of BTA. The average attraction ranges of this combination were 73.3 mm and 81.6 degree in the translation and rotation dimensions, respectively, and the average registration errors were 0.15 mm and 0.32 degree. The WL combination of BT shows the secondary largest attraction ranges. The WL combination of B shows limited convergence property and its attraction range was the smallest among the three examined combinations (on average 33.3 mm and 25.0 degree). If two sets of 3D images in original size (512 x 512) were used, registration could be accomplished within 10~20 minutes by current algorithm, which is only acceptable for off-line reviewing purpose. As the size of image set reduced by a factor of 2~4, the registration time would be 2~4 minutes which is feasible for on-line target localization.
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He, Pan Li, Bo Yang Wang, Xiao Xia Liu, and Xiao Wei Han. "MAP Based Super-Resolution Image Reconstruction Method." Applied Mechanics and Materials 220-223 (November 2012): 2754–57. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2754.
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Super-resolution image reconstruction has been one of the most active research fields in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low- resolution images that have been sub sampled. In the image registration, the paper puts forward an improved search strategies improving registration accuracy. In the MAP algorithm, the threshold parameters of solving the optimal value, making the estimated value of the optimal high-resolution images, so that the reconstructed image is better. The results of the experiments indicate that the proposed algorithm can not only make an automatic choice of the parameter and get the high resolution reconstruction image expected, but also can preserve the edges and details of the image effectively.
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Yang, Han, Xiaorun Li, Liaoying Zhao, and Shuhan Chen. "A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation." Remote Sensing 11, no. 15 (August 6, 2019): 1833. http://dx.doi.org/10.3390/rs11151833.
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Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.
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Yang, H., and X. Li. "A BAND SELECTION METHOD FOR HIGH PRECISION REGISTRATION OF HYPERSPECTRAL IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2067–71. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2067-2018.
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During the registration of hyperspectral images and high spatial resolution images, too much bands in a hyperspectral image make it difficult to select bands with good registration performance. Terrible bands are possible to reduce matching speed and accuracy. To solve this problem, an algorithm based on Cram’er-Rao lower bound theory is proposed to select good matching bands in this paper. The algorithm applies the Cram’er-Rao lower bound theory to the study of registration accuracy, and selects good matching bands by CRLB parameters. Experiments show that the algorithm in this paper can choose good matching bands and provide better data for the registration of hyperspectral image and high spatial resolution image.
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Hikosaka, Shuhei, and Hideyuki Tonooka. "Image-to-Image Subpixel Registration Based on Template Matching of Road Network Extracted by Deep Learning." Remote Sensing 14, no. 21 (October 26, 2022): 5360. http://dx.doi.org/10.3390/rs14215360.
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The vast digital archives collected by optical remote sensing observations over a long period of time can be used to determine changes in the land surface and this information can be very useful in a variety of applications. However, accurate change extraction requires highly accurate image-to-image registration, which is especially true when the target is urban areas in high-resolution remote sensing images. In this paper, we propose a new method for automatic registration between images that can be applied to noisy images such as old aerial photographs taken with analog film, in the case where changes in man-made objects such as buildings in urban areas are extracted from multitemporal high-resolution remote sensing images. The proposed method performs image-to-image registration by applying template matching to road masks extracted from images using a two-step deep learning model. We applied the proposed method to multitemporal images, including images taken more than 36 years before the reference image. As a result, the proposed method achieved registration accuracy at the subpixel level, which was more accurate than the conventional area-based and feature-based methods, even for image pairs with the most distant acquisition times. The proposed method is expected to provide more robust image-to-image registration for differences in sensor characteristics, acquisition time, resolution and color tone of two remote sensing images, as well as to temporal variations in vegetation and the effects of building shadows. These results were obtained with a road extraction model trained on images from a single area, single time period and single platform, demonstrating the high versatility of the model. Furthermore, the performance is expected to be improved and stabilized by using images from different areas, time periods and platforms for training.
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Franz, A., B. Fischer, and S. Kabus. "Spatially Varying Elasticity in Image Registration." Methods of Information in Medicine 46, no. 03 (2007): 287–91. http://dx.doi.org/10.1160/me9045.
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Summary Objectives: In this paper we are concerned with elastic image registration. Usually, elastic approaches assume constant material parameters and result in a smooth displacement field. However, a constant choice has its shortcomings for images with varying elastic properties, like bones and soft tissue. The proposed method allows forspatially varying material properties. Methods: The proposed variational registration scheme is based on a segmentation of the template image. Individual material properties can be assigned to each segmented region. The proposed variable elastic regulariser leads to a displacement field which is adapted to the locally chosen material properties. Results: The capability of this approach is demonstrated by a synthetic and by real-life examples in two dimensions. For all examples the proposed method is compared to a conventional scheme where the material parameters are constants in the entire image domain. Conclusions: A method for non-parametric registration which supports spatially varying elastic properties such as (in)compressibility or Young’s modulus in certain image regions is proposed. It allows for registration results to be more realistic compared to conventional approaches. Also, for a particular structure, an approximated preservation of volume or shape can be achieved.
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Huang, Min, Guanyu Ren, Shizheng Zhang, Qian Zheng, and Huiyang Niu. "An Unsupervised 3D Image Registration Network for Brain MRI Deformable Registration." Computational and Mathematical Methods in Medicine 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/9246378.
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In recent years, deep learning has made successful applications and remarkable achievements in the field of medical image registration, and the method of medical image registration based on deep learning has become the current research hotspot. However, the performance of convolutional neural networks may not be fully exploited due to neglect of spatial relationships between distant locations in the image and incomplete updates of network parameters. To avoid this phenomenon, MHNet, a multiscale hierarchical deformable registration network for 3D brain MR images, was proposed in this paper. This network was an unsupervised end-to-end convolutional neural network. After training, the dense displacement vector field can be predicted almost in real-time for the unseen input image pairs, which saves a lot of time compared with the traditional algorithms of independent iterative optimization for each pair of images. On the basis of the encoder-decoder structure, this network introduced the improved Inception module for multiscale feature extraction and expanding the receptive field and the hierarchical forecast structure to promote the update of the parameters of the middle layers, which achieved the best performance on the augmented public dataset compared with the existing four excellent registration methods.
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Zhou, Cui, Jing Hong Zhou, and Dong Hao Fan. "The Study of a Fast Sub-Pixel Registration Method for Remote Sensing Image." Advanced Materials Research 989-994 (July 2014): 3877–80. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3877.
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We put forward a fast and efficiently sub-pixel registration method for solving the classical methods’ problems of low efficiency, and use efficiently sub-images instead of original image to sub-pixel registration based on the Fourier transform phase correlation and matrix Fourier transform method. Effective sub-images are selected from the total size of the high-frequency energy after two-dimensional wavelet decomposition, then we use the phase correlation to calculate the pixel displacement and matrix Fourier transform to calculate the sub-pixel displacement. Not only the improved method is inherited the advantage of matrix Fourier transform sub-pixel registration, but also the registration speed is greatly improved. This is more applicable to massive remote sensing data. Through simulation and engineering practice, composited registration accuracy and speed, proved that the improved method is more efficient compared with the classical methods, and it’s more suitable for real remote sensing image registration.
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Merkle, N., R. Müller, and P. Reinartz. "REGISTRATION OF OPTICAL AND SAR SATELLITE IMAGES BASED ON GEOMETRIC FEATURE TEMPLATES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 447–52. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-447-2015.
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Image registration is required for different remote sensing applications, like change detection or image fusion. Since research studies have shown the outstanding absolute geometric accuracy of high resolution radar satellites images like TerraSAR-X, the importance of SAR images as source for geolocation enhancement has increased. Due to this fact, multi-sensor image to image registration of optical and SAR images can be used for the improvement of the absolute geometric processing and accuracy of optical images with TerraSAR-X as reference. In comparison to the common optical and SAR image registration methods the proposed method is a combination of intensity-based and feature-based approaches. The proposed method avoids the direct and often difficult detection of features from the SAR images. SAR-like templates are generated from features detected from the optical image. These templates are used for an intensity-based matching with the SAR image. The results of the matching process are ground control points, which are used for the estimation of translation parameters followed by a subpixel translation of the optical image. The proposed image registration method is tested for two pairs of TerraSAR-X and QuickBird images and one pair of TerraSAR-X andWorldView-2 images of a suburban area. The results show that with the proposed method the geometric accuracy of optical images can be enhanced.
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Wei, Ning, Yu He, Junqing Liu, and Peng Chen. "Robust image registration using subspace method in Radon transform domain." Sensor Review 39, no. 5 (September 16, 2019): 645–51. http://dx.doi.org/10.1108/sr-10-2018-0277.
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Purpose The purpose of this paper is to represent a robust image registration method to align noisy and deformed images in their Radon transform domain. Due to the limitation of imaging mechanism, the images are often highly noisy. Even worse, the objects in images have structural differences from time to time. Design/methodology/approach To eliminate these degressions, the proposed method is equipped with subspace-based power spectrum analysis algorithm for rotation estimation and a new global median filter least square algorithm for displacement computation. Findings Experiments on strongly noisy and degenerated images show that the proposed method exhibits better accuracy and robustness than phase correlation-based method. In addition, the method can also be applied to multi-modal registration, where the results are comparable to mutual information method but spending much less time. Originality/value A robust image registration method is proposed, which has better performance than traditional methods.