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Relevant bibliographies by topics / Image reconstruction

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Academic literature on the topic 'Image reconstruction'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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Journal articles on the topic "Image reconstruction"

1

Nestor, Adrian, David C. Plaut, and Marlene Behrmann. "Feature-based face representations and image reconstruction from behavioral and neural data." Proceedings of the National Academy of Sciences 113, no. 2 (December 28, 2015): 416–21. http://dx.doi.org/10.1073/pnas.1514551112.

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The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. Although recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (MRI) patterns, these efforts have relied on a variety of prespecified image features. Here, we take on the twofold task of deriving features directly from empirical data and of using these features for facial image reconstruction. First, we use a method akin to reverse correlation to derive visual features from functional MRI patterns elicited by a large set of homogeneous face exemplars. Then, we combine these features to reconstruct novel face images from the corresponding neural patterns. This approach allows us to estimate collections of features associated with different cortical areas as well as to successfully match image reconstructions to corresponding face exemplars. Furthermore, we establish the robustness and the utility of this approach by reconstructing images from patterns of behavioral data. From a theoretical perspective, the current results provide key insights into the nature of high-level visual representations, and from a practical perspective, these findings make possible a broad range of image-reconstruction applications via a straightforward methodological approach.

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AMERUDDIN, NUR AMALINA, SULAIMAN MD DOM, and MOHD HAFIZI MAHMUD. "EFFECTS OF SMOOTH, MEDIUM SMOOTH AND MEDIUM RECONSTRUCTION KERNELS ON IMAGE QUALITY IN THREE-PHASE CT OF LIVER." Malaysian Applied Biology 50, no. 2 (November 30, 2021): 145–50. http://dx.doi.org/10.55230/mabjournal.v50i2.1974.

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Reconstruction kernel is one of the parameters that affects the computed tomography (CT) image quality. This study aimed to evaluate the effects of applying three different reconstruction kernels on image quality in 3-phased CT of the liver. A total of 63 CT liver images including normal liver (n = 43) and liver lesion (n = 20) were retrospectively reviewed. Smooth (B20f), medium smooth (B30f) and medium (B40f) reconstruction kernels were employed in the image reconstruction process. Mean attenuation, image noise, and signal-to-noise ratio (SNR) values from each kernel reconstruction were quantified and compared among those kernels using One Way Analysis of Variance (ANOVA) statistical analysis. Significant changes in image noise and SNR were observed in the normal liver (p < 0.001, respectively) following the application of those reconstruction kernels. However, no significant changes in mean attenuation, image noise, and SNR were demonstrated in the liver lesion (p > 0.05). Application of smooth (B20f), medium smooth (B30f), and medium (B40f) kernel reconstructions would significantly affect the image noise and SNR in the normal liver of CT images instead of liver lesions. Hence, proper selection of reconstruction kernel is important in CT images reconstruction to improve precision in diagnostic CT interpretation.

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Kazimierczak, Wojciech, Kamila Kędziora, Joanna Janiszewska-Olszowska, Natalia Kazimierczak, and Zbigniew Serafin. "Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality." Journal of Clinical Medicine 13, no. 5 (March 5, 2024): 1502. http://dx.doi.org/10.3390/jcm13051502.

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Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study.

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Bae, Joungeun, and Hoon Yoo. "Image Enhancement for Computational Integral Imaging Reconstruction via Four-Dimensional Image Structure." Sensors 20, no. 17 (August 25, 2020): 4795. http://dx.doi.org/10.3390/s20174795.

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This paper describes the image enhancement of a computational integral imaging reconstruction method via reconstructing a four-dimensional (4-D) image structure. A computational reconstruction method for high-resolution three-dimensional (3-D) images is highly required in 3-D applications such as 3-D visualization and 3-D object recognition. To improve the visual quality of reconstructed images, we introduce an adjustable parameter to produce a group of 3-D images from a single elemental image array. The adjustable parameter controls overlapping in back projection with a transformation of cropping and translating elemental images. It turns out that the new parameter is an independent parameter from the reconstruction position to reconstruct a 4-D image structure with four axes of x, y, z, and k. The 4-D image structure of the proposed method provides more visual information than existing methods. Computer simulations and optical experiments are carried out to show the feasibility of the proposed method. The results indicate that our method enhances the image quality of 3-D images by providing a 4-D image structure with the adjustable parameter.

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Wen, Mingyun, and Kyungeun Cho. "Object-Aware 3D Scene Reconstruction from Single 2D Images of Indoor Scenes." Mathematics 11, no. 2 (January 12, 2023): 403. http://dx.doi.org/10.3390/math11020403.

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Recent studies have shown that deep learning achieves excellent performance in reconstructing 3D scenes from multiview images or videos. However, these reconstructions do not provide the identities of objects, and object identification is necessary for a scene to be functional in virtual reality or interactive applications. The objects in a scene reconstructed as one mesh are treated as a single object, rather than individual entities that can be interacted with or manipulated. Reconstructing an object-aware 3D scene from a single 2D image is challenging because the image conversion process from a 3D scene to a 2D image is irreversible, and the projection from 3D to 2D reduces a dimension. To alleviate the effects of dimension reduction, we proposed a module to generate depth features that can aid the 3D pose estimation of objects. Additionally, we developed a novel approach to mesh reconstruction that combines two decoders that estimate 3D shapes with different shape representations. By leveraging the principles of multitask learning, our approach demonstrated superior performance in generating complete meshes compared to methods relying solely on implicit representation-based mesh reconstruction networks (e.g., local deep implicit functions), as well as producing more accurate shapes compared to previous approaches for mesh reconstruction from single images (e.g., topology modification networks). The proposed method was evaluated on real-world datasets. The results showed that it could effectively improve the object-aware 3D scene reconstruction performance over existing methods.

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Niu, Xiaomei. "Interactive 3D reconstruction method of fuzzy static images in social media." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 806–16. http://dx.doi.org/10.1515/jisys-2022-0049.

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Abstract Because the traditional social media fuzzy static image interactive three-dimensional (3D) reconstruction method has the problem of poor reconstruction completeness and long reconstruction time, the social media fuzzy static image interactive 3D reconstruction method is proposed. For preprocessing the fuzzy static image of social media, the Harris corner detection method is used to extract the feature points of the preprocessed fuzzy static image of social media. According to the extraction results, the parameter estimation algorithm of contrast divergence is used to learn the restricted Boltzmann machine (RBM) network model, and the RBM network model is divided into input, output, and hidden layers. By combining the RBM-based joint dictionary learning method and a sparse representation model, an interactive 3D reconstruction of fuzzy static images in social media is achieved. Experimental results based on the CAD software show that the proposed method has a reconstruction completeness of above 95% and the reconstruction time is less than 15 s, improving the completeness and efficiency of the reconstruction, effectively reconstructing the fuzzy static images in social media, and increasing the sense of reality of social media images.

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Liu, Xueyan, Limei Zhang, Yining Zhang, and Lishan Qiao. "A Photoacoustic Imaging Algorithm Based on Regularized Smoothed L0 Norm Minimization." Molecular Imaging 2021 (June 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/6689194.

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The recently emerging technique of sparse reconstruction has received much attention in the field of photoacoustic imaging (PAI). Compressed sensing (CS) has large potential in efficiently reconstructing high-quality PAI images with sparse sampling signal. In this article, we propose a CS-based error-tolerant regularized smooth L0 (ReSL0) algorithm for PAI image reconstruction, which has the same computational advantages as the SL0 algorithm while having a higher degree of immunity to inaccuracy caused by noise. In order to evaluate the performance of the ReSL0 algorithm, we reconstruct the simulated dataset obtained from three phantoms. In addition, a real experimental dataset from agar phantom is also used to verify the effectiveness of the ReSL0 algorithm. Compared to three L0 norm, L1 norm, and TV norm-based CS algorithms for signal recovery and image reconstruction, experiments demonstrated that the ReSL0 algorithm provides a good balance between the quality and efficiency of reconstructions. Furthermore, the PSNR of the reconstructed image calculated by the introduced method was better than the other three methods. In particular, it can notably improve reconstruction quality in the case of noisy measurement.

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Wang, Xuan, Lijun Sun, Abdellah Chehri, and Yongchao Song. "A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images." Remote Sensing 15, no. 20 (October 21, 2023): 5062. http://dx.doi.org/10.3390/rs15205062.

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High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant research on generative adversarial networks in high-resolution image reconstruction. We cover various aspects, such as datasets, evaluation criteria, and conventional models used for image reconstruction. Subsequently, the super-resolution reconstruction models based on generative adversarial networks are categorized based on whether the kernel blurring function is recognized and utilized during training. We provide a brief overview of the utilization of generative adversarial network models in analyzing remote sensing imagery. In conclusion, we present a prospective analysis of forthcoming research directions pertaining to super-resolution reconstruction methods that rely on generative adversarial networks.

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Xiang-Yun Yi, Xiang-Yun Yi, Xiao-Bo Dong Xiang-Yun Yi, Liang-Gui Zhang Xiao-Bo Dong, Yan-Chao Sun Liang-Gui Zhang, Wen-Tao Li Yan-Chao Sun, and Tao Zhang Wen-Tao Li. "Compressive Perception Image Reconstruction Technology for Basic Mixed Sparse Basis in Metal Surface Detection." 電腦學刊 35, no. 1 (February 2024): 159–65. http://dx.doi.org/10.53106/199115992024023501011.

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<p>Applying Compressed Sensing (CS) technology to robot vision image transmission, an effective method for image reconstruction in robot imaging is proposed to improve the accuracy of reconstruction. Reconstructing images using a mixed sparse representation of DCT and circularly symmetric contour wave transform, the basic algorithm used is the Smoothed Projection Landweber (SPL) algorithm, which optimizes the coefficients under different sparse transformations by incorporating hard thresholding and binary thresholding methods for different sparse bases during iterations. The experiment shows that compared with single sparse base image reconstruction, the proposed reconstruction method has improved reconstruction accuracy.</p> <p> </p>

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Seetharamaswamy, Shashi Kiran, and Suresh Kaggere Veeranna. "Image reconstruction through compressive sampling matching pursuit and curvelet transform." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 2023): 6277. http://dx.doi.org/10.11591/ijece.v13i6.pp6277-6284.

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An interesting area of research is image reconstruction, which uses algorithms and techniques to transform a degraded image into a good one. The quality of the reconstructed image plays a vital role in the field of image processing. Compressive Sampling is an innovative and rapidly growing method for reconstructing signals. It is extensively used in image reconstruction. The literature uses a variety of matching pursuits for image reconstruction. In this paper, we propose a modified method named compressive sampling matching pursuit (CoSaMP) for image reconstruction that promises to sample sparse signals from far fewer observations than the signal’s dimension. The main advantage of CoSaMP is that it has an excellent theoretical guarantee for convergence. The proposed technique combines CoSaMP with curvelet transform for better reconstruction of image. Experiments are carried out to evaluate the proposed technique on different test images. The results indicate that qualitative and quantitative performance is better compared to existing methods.

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Dissertations / Theses on the topic "Image reconstruction"

1

Jubb, M. D. "Image reconstruction." Thesis, University of Bath, 1989. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328818.

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Nasir, Haidawati Mohamad. "Super-resolution image reconstruction from low-resolution images." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=17814.

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The thesis addresses the problem of obtaining high-resolution image from a set of one or more low-resolution images. The thesis focused on three building blocks of super-resolution algorithms i.e., image registration for super-resolution, image fusion for super-resolution and super-resolution image reconstruction. These three parts are addressed separately and singular value decomposition-based fusion is introduced before performing interpolation or single-image super-resolution. An accurate image registration is crucial for super-resolution. An image registration approach for super-resolution based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is described to automatically register the low-resolution images. The results have shown effective for the removal of the mismatched features in the image. A novel SVD-based image fusion for super-resolution is developed for integrating the significant features from low-resolution images. The SVD-based image fusion is shown to enhance the super-resolution results. The implementation of a novel interpolation method based on a linear combination of the bicubic interpolation and their first-order derivates and the use of first-order difference equation to extract the features from the low-resolution images are described and shown to improve the method of single image super-resolution using sparse representation. The proposed method has shown to reduces the computational time and enhance the prior estimation of the high-resolution image as well as the final super-resolution results. The performance of the algorithms is evaluated using synthetic sequences and also on real sequences subjectively and objectively.

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施能強 and Nang-keung Sze. "Image reconstruction with multisensors." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226711.

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Sze, Nang-keung. "Image reconstruction with multisensors /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23621552.

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Azzari, Pietro <1979&gt. "Reconstruction from image correspondences." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1259/1/Azzari_Pietro_Reconstruction_from_image_correspondences.pdf.

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A single picture provides a largely incomplete representation of the scene one is looking at. Usually it reproduces only a limited spatial portion of the scene according to the standpoint and the viewing angle, besides it contains only instantaneous information. Thus very little can be understood on the geometrical structure of the scene, the position and orientation of the observer with respect to it remaining also hard to guess. When multiple views, taken from different positions in space and time, observe the same scene, then a much deeper knowledge is potentially achievable. Understanding inter-views relations enables construction of a collective representation by fusing the information contained in every single image. Visual reconstruction methods confront with the formidable, and still unanswered, challenge of delivering a comprehensive representation of structure, motion and appearance of a scene from visual information. Multi-view visual reconstruction deals with the inference of relations among multiple views and the exploitation of revealed connections to attain the best possible representation. This thesis investigates novel methods and applications in the field of visual reconstruction from multiple views. Three main threads of research have been pursued: dense geometric reconstruction, camera pose reconstruction, sparse geometric reconstruction of deformable surfaces. Dense geometric reconstruction aims at delivering the appearance of a scene at every single point. The construction of a large panoramic image from a set of traditional pictures has been extensively studied in the context of image mosaicing techniques. An original algorithm for sequential registration suitable for real-time applications has been conceived. The integration of the algorithm into a visual surveillance system has lead to robust and efficient motion detection with Pan-Tilt-Zoom cameras. Moreover, an evaluation methodology for quantitatively assessing and comparing image mosaicing algorithms has been devised and made available to the community. Camera pose reconstruction deals with the recovery of the camera trajectory across an image sequence. A novel mosaic-based pose reconstruction algorithm has been conceived that exploit image-mosaics and traditional pose estimation algorithms to deliver more accurate estimates. An innovative markerless vision-based human-machine interface has also been proposed, so as to allow a user to interact with a gaming applications by moving a hand held consumer grade camera in unstructured environments. Finally, sparse geometric reconstruction refers to the computation of the coarse geometry of an object at few preset points. In this thesis, an innovative shape reconstruction algorithm for deformable objects has been designed. A cooperation with the Solar Impulse project allowed to deploy the algorithm in a very challenging real-world scenario, i.e. the accurate measurements of airplane wings deformations.

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6

Azzari, Pietro <1979&gt. "Reconstruction from image correspondences." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1259/.

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A single picture provides a largely incomplete representation of the scene one is looking at. Usually it reproduces only a limited spatial portion of the scene according to the standpoint and the viewing angle, besides it contains only instantaneous information. Thus very little can be understood on the geometrical structure of the scene, the position and orientation of the observer with respect to it remaining also hard to guess. When multiple views, taken from different positions in space and time, observe the same scene, then a much deeper knowledge is potentially achievable. Understanding inter-views relations enables construction of a collective representation by fusing the information contained in every single image. Visual reconstruction methods confront with the formidable, and still unanswered, challenge of delivering a comprehensive representation of structure, motion and appearance of a scene from visual information. Multi-view visual reconstruction deals with the inference of relations among multiple views and the exploitation of revealed connections to attain the best possible representation. This thesis investigates novel methods and applications in the field of visual reconstruction from multiple views. Three main threads of research have been pursued: dense geometric reconstruction, camera pose reconstruction, sparse geometric reconstruction of deformable surfaces. Dense geometric reconstruction aims at delivering the appearance of a scene at every single point. The construction of a large panoramic image from a set of traditional pictures has been extensively studied in the context of image mosaicing techniques. An original algorithm for sequential registration suitable for real-time applications has been conceived. The integration of the algorithm into a visual surveillance system has lead to robust and efficient motion detection with Pan-Tilt-Zoom cameras. Moreover, an evaluation methodology for quantitatively assessing and comparing image mosaicing algorithms has been devised and made available to the community. Camera pose reconstruction deals with the recovery of the camera trajectory across an image sequence. A novel mosaic-based pose reconstruction algorithm has been conceived that exploit image-mosaics and traditional pose estimation algorithms to deliver more accurate estimates. An innovative markerless vision-based human-machine interface has also been proposed, so as to allow a user to interact with a gaming applications by moving a hand held consumer grade camera in unstructured environments. Finally, sparse geometric reconstruction refers to the computation of the coarse geometry of an object at few preset points. In this thesis, an innovative shape reconstruction algorithm for deformable objects has been designed. A cooperation with the Solar Impulse project allowed to deploy the algorithm in a very challenging real-world scenario, i.e. the accurate measurements of airplane wings deformations.

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Zeng, Gang. "Surface reconstruction from images /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20ZENG.

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Rhoden, Christopher A. "Linear optimization and image reconstruction." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283641.

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莫紹祥 and Siu-cheung Mok. "Parametric halftoning and image reconstruction." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1988. http://hub.hku.hk/bib/B31208800.

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Cui, Xuelin. "Joint CT-MRI Image Reconstruction." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86177.

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Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed information than ever. Aside from the evolution of hardware and software, multimodal imaging techniques offer a promising solution to produce higher quality images by fusing medical images from different modalities. This strategy utilizes more structural and/or functional image information, thereby allowing clinical results to be more comprehensive and better interpreted. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. The method proposed in this study is part of the planned joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI platform. However, since their image data are highly undersampled, analytical methods, such as filtered backprojection, are unable to generate images of sufficient quality. To overcome this drawback, we resort to compressed sensing techniques, which employ sparse priors that result from an application of L1-norm minimization. To utilize multimodal information, a projection distance is introduced and is tuned to tailor the texture and pattern of final images. Specifically CT and MRI images are alternately reconstructed using the updated multimodal results that are calculated at the latest step of the iterative optimization algorithm. This method exploits the structural similarities shared by the CT and MRI images to achieve better reconstruction quality. The improved performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI body images and a pair of undersampled CT-MRI head images. These images are tested using joint reconstruction, analytical reconstruction, and independent reconstruction without using multimodal imaging information. Results show that the proposed method improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity measurements compared to independent reconstruction methods. It offers a similar quality as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and a 30% sampling rate for MRI. It is concluded that structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of image reconstruction.
Ph. D.
Medical imaging techniques play a central role in modern clinical diagnoses and treatments. Consequently, there is a constant demand to increase the overall quality of medical images. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. Multimodal imaging techniques can provide more detailed diagnostic information by fusing medical images from different imaging modalities, thereby allowing clinical results to be more comprehensive to improve clinical interpretation. A new form of multimodal imaging technique, which combines the imaging procedures of computed tomography (CT) and magnetic resonance imaging (MRI), is known as the “omnitomography.” Both computed tomography and magnetic resonance imaging are the most commonly used medical imaging techniques today and their intrinsic properties are complementary. For example, computed tomography performs well for bones whereas the magnetic resonance imaging excels at contrasting soft tissues. Therefore, a multimodal imaging system built upon the fusion of these two modalities can potentially bring much more information to improve clinical diagnoses. However, the planned omni-tomography systems face enormous challenges, such as the limited ability to perform image reconstruction due to mechanical and hardware restrictions that result in significant undersampling of the raw data. Image reconstruction is a procedure required by both computed tomography and magnetic resonance imaging to convert raw data into final images. A general condition required to produce a decent quality of an image is that the number of samples of raw data must be sufficient and abundant. Therefore, undersampling on the omni-tomography system can cause significant degradation of the image quality or artifacts after image reconstruction. To overcome this drawback, we resort to compressed sensing techniques, which exploit the sparsity of the medical images, to perform iterative based image reconstruction for both computed tomography and magnetic resonance imaging. The sparsity of the images is found by applying sparse transform such as discrete gradient transform or wavelet transform in the image domain. With the sparsity and undersampled raw data, an iterative algorithm can largely compensate for the data inadequacy problem and it can reconstruct the final images from the undersampled raw data with minimal loss of quality. In addition, a novel “projection distance” is created to perform a joint reconstruction which further promotes the quality of the reconstructed images. Specifically, the projection distance exploits the structural similarities shared between the image of computed tomography and magnetic resonance imaging such that the insufficiency of raw data caused by undersampling is further accounted for. The improved performance of the proposed approach is demonstrated using a pair of undersampled body images and a pair of undersampled head images, each of which consists of an image of computed tomography and its magnetic resonance imaging counterpart. These images are tested using the proposed joint reconstruction method in this work, the conventional reconstructions such as filtered backprojection and Fourier transform, and reconstruction strategy without using multimodal imaging information (independent reconstruction). The results from this work show that the proposed method addressed these challenges by significantly improving the image quality from highly undersampled raw data. In particular, it improves about 5dB in signal-to-noise ratio and nearly 10% in structural similarity measurements compared to other methods. It achieves similar image quality by using less than 5% of the X-ray dose for computed tomography and 30% sampling rate for magnetic resonance imaging. It is concluded that, by using compressed sensing techniques and exploiting structural similarities, the planned joint computed tomography and magnetic resonance imaging system can perform imaging outstanding tasks with highly undersampled raw data.

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Books on the topic "Image reconstruction"

1

Zeng, Gengsheng Lawrence. Medical Image Reconstruction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05368-9.

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J, McDonnell M., ed. Image restoration and reconstruction. Oxford [Oxfordshire]: Clarendon Press, 1986.

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International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, (2nd : 1993 : Snowbird, Utah), ed. Fully 3D image reconstruction. Bristol: IOP Publishing, 1994.

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Image reconstruction in radiology. Boca Raton, Fla: CRC Press, 1990.

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Sá, Asla M., Paulo Cezar Carvalho, and Luiz Velho. High Dynamic Range Image Reconstruction. Cham: Springer International Publishing, 2007. http://dx.doi.org/10.1007/978-3-031-79522-0.

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Korostelev, A. P., and A. B. Tsybakov. Minimax Theory of Image Reconstruction. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-2712-0.

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1941-, Natterer F., and Wübbeling Frank, eds. Mathematical methods in image reconstruction. Philadelphia: Society for Industrial and Applied Mathematics, 2001.

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Korostelev, A. P. Minimax theory of image reconstruction. New York: Springer-Verlag, 1993.

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Nowiński, Wiesław Lucjan. Asynchronism in parallel image reconstruction. Warszawa: Instytut Podstaw Informatyki Polskiej Akademii Nauk, 1990.

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Rhoden, Christopher A. Linear optimization and image reconstruction. Monterey, Calif: Naval Postgraduate School, 1994.

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Book chapters on the topic "Image reconstruction"

1

Gu, Min, Xiaosong Gan, and Xiaoyuan Deng. "Image Reconstruction." In Microscopic Imaging Through Turbid Media, 175–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46397-0_9.

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Saha, Gopal B. "Image Reconstruction." In Basics of PET Imaging, 71–85. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0805-6_4.

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Saha, Gopal B. "Image Reconstruction." In Basics of PET Imaging, 91–107. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-16423-6_4.

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Comtat, Claude. "Image Reconstruction." In Handbook of Particle Detection and Imaging, 973–1006. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-13271-1_39.

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Prandi, Dario, and Jean-Paul Gauthier. "Image Reconstruction." In SpringerBriefs in Mathematics, 77–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78482-3_6.

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Comtat, Claude. "Image Reconstruction." In Handbook of Particle Detection and Imaging, 1–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-47999-6_39-2.

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Comtat, Claude. "Image Reconstruction." In Handbook of Particle Detection and Imaging, 1279–316. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-93785-4_39.

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Pavone, Paolo. "Image Reconstruction." In Imaging Coronary Arteries, 29–40. Milano: Springer Milan, 2013. http://dx.doi.org/10.1007/978-88-470-2682-7_4.

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Lannes, A. "Image Reconstruction." In High Angular Resolution in Astrophysics, 115–43. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0041-7_7.

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Zeng, Gengsheng Lawrence. "Iterative Reconstruction." In Medical Image Reconstruction, 125–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05368-9_6.

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Conference papers on the topic "Image reconstruction"

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Rolleston, Robert, and Nicholas George. "Image reconstruction from partial Fresnel zone information." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tur6.

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An iterative algorithm for reconstructing an image from partial Fresnel zone information is discussed. With the standard 4-F canonical optical processor, processing is done midway between the two lenses in the Fourier transform plane. While others have studied reconstruction from partial Fourier plane information, we have investigated methods of reconstructing an object from partial information in the Fresnel region of an optical processor. Efficient digital calculation for integrations with a Fresnel-zone type of kernel are described. Iterative algorithms for reconstructing an object from either the phase or magnitude of the Fresnel zone transform are discussed. In the case of reconstructing an image from the Fresnel zone magnitude, we obtain good images in fewer iterations at locations which are farther from the Fourier plane. Reconstructing an image from the Fresnel zone phase is fairly insensitive to this shift out of the Fourier plane. In another investigation, we start with the assumption that the object has been coded into an unknown location in the Fresnel region. We describe an iterative searching technique that locates this position; thereafter we reconstruct the image.

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Tzannes, N. S., John S. Bodenscharz, and M. A. Tzannes. "Image reconstruction using entropy, relative entropy, and the discrete cosine transform." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.mo3.

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Previously1 we introduced the use of the maximum entropy principle (MEP) for image reconstruction of moment-coded images. The MEP and the minimum relative principle (MREP) are used in reconstructing images previously compressed by retaining a subset of the coefficients of their discrete cosine transform (DCT), a popular method of image compression since it approximates closely the ideal Karhunen-Loeve transform compression. The normal way to reconstruct such compressed images is by using the inverse DCT. The reconstructed image under the MEP is the one that maximizes the entropy of the image subject to constraints reflecting the retained DCT coefficients. Under the MREP, the reconstructed image is obtained by successive minimization of its relative entropy subject to one constraint at a time, with each solution serving as a prior for the next minimization. Both methods were applied to images compressed by DCT, and the results were compared to normal inverse DCT reconstruction. It is concluded that MEP and MREP are better than IDCT, and the improvement is attributed to the fact that these methods make no assumptions about the value of the unretained coefficients, while IDCT assumes them equal to zero.

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Mandal, Mita, U. B. Desai, M. P. Thaddeus, and Gargi Vishnoi. "Optical image reconstruction of inhomogeneities in tissue." In Image Reconstruction from Incomplete Data IV. SPIE, 2006. http://dx.doi.org/10.1117/12.680015.

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Boora, Shivaprasad, Bharat Ch Sahu, and Dipti Patra. "3D image reconstruction from multiview images." In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2017. http://dx.doi.org/10.1109/icccnt.2017.8204120.

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Wenqi, Gao, Tan Suqing, and Zhou Jin. "Computer-generated hologram for reconstruction of unusual mode image." In Diffractive Optics and Micro-Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/domo.1996.jtub.26.

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Usually in reconstruction of Fourier computer-generated hologram[FCGH] the lens is necessary to make imaging at finite distance instead of imaging at infinite originally. The reconstructed images are mutual inverted ( one upright image,another inverted image) both appear in a same plane. Whether the imaging lens in reconstruction FCGH will be able to omit? Whether two reconstructed images will be able to separate in spatial? Whether two images have an identical direction and different shape in the same plane? It is the motivation for us to do this study. Through theoretic analysis and experimental reconstruction these assume can be realized essentially.

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C. Singh, S., and C. H. Chapman. "Slowness image reconstruction." In 56th EAEG Meeting. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201410020.

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Tumblin, Jack. "Image reconstruction techniques." In ACM SIGGRAPH 2006 Courses. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1185657.1185736.

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Sheppard, CJR. "Microscope image reconstruction." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.stue.2.

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In brightfield, phase-contrast or polarization microscopy, the image can be modeled by using scattering theory. The object, consisting of spatial variations in complex refractive index, scatters components of an angular spectrum of plane waves, and the image calculated by integration over incident and scattered waves. This approach takes into account the high aperture effects, important in microscope imaging. Rigorous methods can be used to calculate the scattering by the object.1 However, these methods, in addition to being in general very computationally intensive, result in the disadvantges that it is difficult to see trends in the behaviour and usually impracticable to reconstruct the object from the image data.

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Stark, Henry, and Peyma Oskoui-Fard. "High resolution image reconstruction from image plane arrays." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.fn3.

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When image plane detectors are larger than the blur spot of the imaging optics, postdetection processing is required to reconstruct images with resolution commensurate with the imaging optics. We use the method of convex projections to increase image resolution by using each detector reading as a constraint set and projecting iteratively among these sets. One significant advantage of using convex projections is that the effect of dead detectors is less severe than in algorithms based on least-squares or localized backprojections.1

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Aitken, G. J. M., R. Johnson, and J. Meng. "Speckle-image, phase reconstruction techniques compared." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.tup1.

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The Knox-Thompson (KT), triple-correlation (TC), and phase-gradient (PG) algorithms have been shown capable of reconstructing the phase of stellar spatial spectra upto the diffraction limit of large telescopes. Combined with the modulus measurements of Labeyrie’s speckle interferometry, these techniques offer from 10 to 50 times improvement in image resolution over the 2-sec of arc limit imposed on conventional astronomical observations by atmospheric turbulence. We report results of a comparative study in which the performance of the three techniques is evaluated with a common set of photon-limited speckle-image data from both simulated and real astronomical sources. The regime of interest is < 1 photon/ speckle and <500 photons/image. In these conditions PG gives excellent reconstructed images and is the most computationally efficient of the three. KT gives slightly poorer results in severe atmospheric conditions, can be improved by image centroiding, but experiences a further distorting effect at very low photon levels. TC requires the greatest computational effort but appears to be more robust with respect to certain types of data defect. Extension of TC can give a small improvement in image quality but at a high computational cost.

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Reports on the topic "Image reconstruction"

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Strittmatter, P. A., and E. K. Hege. Speckle Image Reconstruction. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada158653.

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Chartrand, Rick. Image processing and reconstruction. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1044085.

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Fienup, J. R., and J. H. Seldin. Image Reconstruction from Interferometer Data. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada268463.

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Chambers, David H. Polarimetric ISAR: Simulation and image reconstruction. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1247281.

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Juengling, Ralf. Advances in Piecewise Smooth Image Reconstruction. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1670.

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Forber, Richard, and Uzi Efron. Optoelectronic Computer Architecture Development for Image Reconstruction. Fort Belvoir, VA: Defense Technical Information Center, October 1996. http://dx.doi.org/10.21236/ada329541.

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Nunez, J., and J. Llacer. Bayesian image reconstruction: Application to emission tomography. Office of Scientific and Technical Information (OSTI), February 1989. http://dx.doi.org/10.2172/6609701.

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Vogel, Curtis R. Computational Methods for Image Reconstruction and Enhancement. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada381745.

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Blankenbecler, Richard. 3D Image Reconstruction: Determination of Pattern Orientation. Office of Scientific and Technical Information (OSTI), March 2003. http://dx.doi.org/10.2172/812988.

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Nguyen, Hung. Optical Stitching and Image Reconstruction Imaging Systems. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1487439.

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