Relevant bibliographies by topics / Super-resolved image reconstruction

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  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Super-resolved image reconstruction'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Super-resolved image reconstruction"

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Sahithi, V. S., and S. Agrawal. "Sub pixel location identification using super resolved multilooking CHRIS data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 463–68. http://dx.doi.org/10.5194/isprsarchives-xl-8-463-2014.

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CHRIS /Proba is a multiviewing hyperspectral sensor that monitors the earth in five different zenith angles +55°, +36°, nadir, −36° and −55° with a spatial resolution of 17 m and within a spectral range of 400–1050 nm in mode 3. These multiviewing images are suitable for constructing a super resolved high resolution image that can reveal the mixed pixel of the hyperspectral image. In the present work, an attempt is made to find the location of various features constituted within the 17m mixed pixel of the CHRIS image using various super resolution reconstruction techniques. Four different super resolution reconstruction techniques namely interpolation, iterative back projection, projection on to convex sets (POCS) and robust super resolution were tried on the −36, nadir and +36 images to construct a super resolved high resolution 5.6 m image. The results of super resolution reconstruction were compared with the scaled nadir image and bicubic convoluted image for comparision of the spatial and spectral property preservance. A support vector machine classification of the best super resolved high resolution image was performed to analyse the location of the sub pixel features. Validation of the obtained results was performed using the spectral unmixing fraction images and the 5.6 m classified LISS IV image.
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Pashaei, Mohammad, Michael J. Starek, Hamid Kamangir, and Jacob Berryhill. "Deep Learning-Based Single Image Super-Resolution: An Investigation for Dense Scene Reconstruction with UAS Photogrammetry." Remote Sensing 12, no. 11 (May 29, 2020): 1757. http://dx.doi.org/10.3390/rs12111757.

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The deep convolutional neural network (DCNN) has recently been applied to the highly challenging and ill-posed problem of single image super-resolution (SISR), which aims to predict high-resolution (HR) images from their corresponding low-resolution (LR) images. In many remote sensing (RS) applications, spatial resolution of the aerial or satellite imagery has a great impact on the accuracy and reliability of information extracted from the images. In this study, the potential of a DCNN-based SISR model, called enhanced super-resolution generative adversarial network (ESRGAN), to predict the spatial information degraded or lost in a hyper-spatial resolution unmanned aircraft system (UAS) RGB image set is investigated. ESRGAN model is trained over a limited number of original HR (50 out of 450 total images) and virtually-generated LR UAS images by downsampling the original HR images using a bicubic kernel with a factor × 4 . Quantitative and qualitative assessments of super-resolved images using standard image quality measures (IQMs) confirm that the DCNN-based SISR approach can be successfully applied on LR UAS imagery for spatial resolution enhancement. The performance of DCNN-based SISR approach for the UAS image set closely approximates performances reported on standard SISR image sets with mean peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index values of around 28 dB and 0.85 dB, respectively. Furthermore, by exploiting the rigorous Structure-from-Motion (SfM) photogrammetry procedure, an accurate task-based IQM for evaluating the quality of the super-resolved images is carried out. Results verify that the interior and exterior imaging geometry, which are extremely important for extracting highly accurate spatial information from UAS imagery in photogrammetric applications, can be accurately retrieved from a super-resolved image set. The number of corresponding keypoints and dense points generated from the SfM photogrammetry process are about 6 and 17 times more than those extracted from the corresponding LR image set, respectively.
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Xiang, Peng-Cheng, Cong-Bo Cai, Jie-Chao Wang, Shu-Hui Cai, and Zhong Chen. "Super-resolved reconstruction method for spatiotemporally encoded magnetic resonance imaging based on deep neural network." Acta Physica Sinica 71, no. 5 (2022): 058702. http://dx.doi.org/10.7498/aps.71.20211754.

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Single-shot spatiotemporally-encoded magnetic resonance imaging (SPEN MRI) is a novel ultrafast MRI technology. The SPEN MRI possesses great resistance to inhomogeneous <i>B</i><sub>0</sub> magnetic field and chemical shift effect. However, it has inherently low spatial resolution, and the super-resolved reconstruction is required to improve the spatial resolution of SPEN MRI image without additional signal acquisition. Several super-resolved reconstruction methods have been proposed, but they all suffer the problems of long iterative solution time and/or aliasing artifacts residue in the reconstructed results. In this paper, a super-resolved reconstruction method is proposed for single-shot SPEN MRI based on deep neural network. In this method the simulation samples are used to train the deep neural network, and then the trained network model is adopted to reconstruct the real sampled signals. Experimental results of numerical simulation, water phantom and in vivo rat brain show that this method can quickly reconstruct a super-resolved SPEN image with no residual aliasing artifacts, and clear texture information. An appropriate number of training samples and an appropriate random noise level for training samples contribute to improving the reconstruction results.
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Wang, Bowen. "Infrared fiber bundle image super-resolved based on computational imaging." Journal of Physics: Conference Series 2478, no. 6 (June 1, 2023): 062009. http://dx.doi.org/10.1088/1742-6596/2478/6/062009.

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Abstract As the representative of flexibility in optical imaging media, in recent years, fiber bundles have emerged as a promising architecture in the development of compact visual systems. Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio (SNR) imaging in fiber bundles, the iterative super-resolution reconstruction network based on a physical model is proposed. Under the constraint of solving the two subproblems of data fidelity and prior regular term alternately, the network can efficiently “regenerate” the lost spatial resolution with deep learning. By building and calibrating a dual-path imaging system, the real-world dataset where paired low resolution (LR) - high resolution (HR) images on the same scene can be obtained simultaneously. Numerical results on both USAF target and complex target objects demonstrate that the algorithm can restore high contrast images without pixilated noise. On the basis of super-resolution reconstruction, compound eye image composition based on fiber bundle is also realized in this paper for the actual imaging requirements. The proposed work is the first to apply a physical model-based network structure to fiber bundle imaging in the long-wave infrared band, effectively promoting the engineering application of thermal radiation detection.
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Čapek, Martin, Michaela Blažíková, Ivan Novotný, Helena Chmelová, David Svoboda, Barbora Radochová, Jiří Janáček, and Ondrej Horváth. "The Wavelet-Based Denoising Of Images in Fiji, With Example Applications in Structured Illumination Microscopy." Image Analysis & Stereology 40, no. 1 (April 9, 2021): 3–16. http://dx.doi.org/10.5566/ias.2432.

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Filtration of super-resolved microscopic images brings often troubles with removing undesired image parts like, e.g., noise, inhomogenous background and reconstruction artifacts. Standard filtration techniques, e.g., convolution- or Fourier transform-based methods are not always appropriate, since they may lower image resolution that was acquired by hi-tech and expensive microscopy systems. Thus, in this article it is proposed to filter such images using discrete wavelet transform (DWT). Newly developed Wavelet_Denoise plugin for free available Fiji software package demonstrates important possibilities of applying DWT to images: Decomposition of a filtered picture using various wavelet filters and levels of details with showing decomposed images and visualization of effects of back transformation of the picture with chosen level of suppression or denoising of wavelet coefficients. The Fiji framework allows, for example, using a plethora of various microscopic image formats for data opening, users can easily install the plugin through a menu command and the plugin supports processing 3D images in Z-stacks. The application of the plugin for removal of reconstruction artifacts and undesirable background in images acquired by super-resolved structured illumination microscopy is demonstrated as well.
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Ahn, Il Jun, Ji Hye Kim, YongJin Chang, Kye Young Jeong, and Jong Beom Ra. "LOR-Based Reconstruction for Super-Resolved 3D PET Image on GPU." IEEE Transactions on Nuclear Science 62, no. 3 (June 2015): 859–68. http://dx.doi.org/10.1109/tns.2015.2421908.

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Yang, Xue, Feng Li, Lei Xin, Xiaotian Lu, Ming Lu, and Nan Zhang. "An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites." Remote Sensing 12, no. 3 (February 2, 2020): 466. http://dx.doi.org/10.3390/rs12030466.

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Super-resolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve ill-conditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the gray-level variation between adjacent pixels, and the signal-to-noise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSR-NCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified.
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Han, Sujy, Tae Bok Lee, and Yong Seok Heo. "Deep Image Prior for Super Resolution of Noisy Image." Electronics 10, no. 16 (August 20, 2021): 2014. http://dx.doi.org/10.3390/electronics10162014.

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Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.
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Eadie, Matthew, Jinpeng Liao, Wael Ageeli, Ghulam Nabi, and Nikola Krstajić. "Fiber Bundle Image Reconstruction Using Convolutional Neural Networks and Bundle Rotation in Endomicroscopy." Sensors 23, no. 5 (February 23, 2023): 2469. http://dx.doi.org/10.3390/s23052469.

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Fiber-bundle endomicroscopy has several recognized drawbacks, the most prominent being the honeycomb effect. We developed a multi-frame super-resolution algorithm exploiting bundle rotation to extract features and reconstruct underlying tissue. Simulated data was used with rotated fiber-bundle masks to create multi-frame stacks to train the model. Super-resolved images are numerically analyzed, which demonstrates that the algorithm can restore images with high quality. The mean structural similarity index measurement (SSIM) improved by a factor of 1.97 compared with linear interpolation. The model was trained using images taken from a single prostate slide, 1343 images were used for training, 336 for validation, and 420 for testing. The model had no prior information about the test images, adding to the robustness of the system. Image reconstruction was completed in 0.03 s for 256 × 256 images indicating future real-time performance is within reach. The combination of fiber bundle rotation and multi-frame image enhancement through machine learning has not been utilized before in an experimental setting nut could provide a much-needed improvement to image resolution in practice.
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Liu, Shanshan, Qingbin Huang, and Minghui Wang. "Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation." Applied Sciences 12, no. 20 (October 20, 2022): 10606. http://dx.doi.org/10.3390/app122010606.

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Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images. Inaccurate blur kernel estimation will enlarge the distortion of the estimated high-resolution image. Therefore, multi-frame blind super resolution with unknown blur kernel is more challenging. For the purpose of reducing the impact of inaccurate motion estimation and blur kernel estimation on the super-resolved image, we propose a novel method combining motion estimation, blur kernel estimation and super resolution. The confidence weight of low-resolution images and the parameter value of the motion model obtained in image reconstruction are added to the modified motion estimation and blur kernel estimation. At the same time, Jacobian matrix, which can better describe the motion change, is introduced to further correct the error of motion estimation. Based on the results acquired from the experiments on synthetic data and real data, the superiority of the proposed method over others is obvious. The reconstructed high-resolution image retains the details of the image effectively, and the artifacts are greatly reduced.
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Dissertations / Theses on the topic "Super-resolved image reconstruction"

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Ghulyani, Manu. "Fast total variation minimizing image restoration under mixed Poisson-Gaussian noise." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4477.

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Image acquisition in many biomedical imaging modalities is corrupted by Poisson noise followed by additive Gaussian noise. Maximum Likelihood Estimation (MLE) based restoration methods that use the exact Likelihood function for this mixed model with non-quadratic regularization are very few. In particular, while it has been demonstrated that total variation (TV) based regularization methods give better results, such methods that use exact Poisson-Gaussian Likelihood are slow. In this thesis, an ADMM (Alternating Direction Method of Multipliers) based fast algorithm was proposed for image restoration using exact Poisson-Gaussian Likelihood function and TV regularization. Speci fically, this thesis work describes a novel variable splitting approach that enables isolating the complexity in the exact log-likelihood functional from the image blurring operation, allowing a fast Newton-like iteration on the log-likelihood functional. This leads to a signi ficantly improved convergence rate of the overall ADMM iteration. Suffcient conditions for convergence of this algorithm are also derived as a part of the thesis. Expectation-Minimization based iterations were deployed to further exploit the proposed splitting approach. The effectiveness of the proposed methods was demonstrated using restoration examples. An extension to this method for super-resolved image reconstruction for structured illumination microscopy (SIM) was proposed. In SIM, extension of resolution beyond diffraction limit is achieved by illuminating the sample with a sinusoidal pattern. While known practical methods achieve reconstruction for SIM by modifying the measured data with sinusoidal modulation followed by a regularized multi-PSF deconvolution, the proposed approach achieves reconstruction by means of TV penalized MLE with exact likelihood composed of raw measured data.
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Book chapters on the topic "Super-resolved image reconstruction"

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Cheeseman, Peter, Bob Kanefsky, Richard Kraft, John Stutz, and Robin Hanson. "Super-Resolved Surface Reconstruction from Multiple Images." In Maximum Entropy and Bayesian Methods, 293–308. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8729-7_23.

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Conference papers on the topic "Super-resolved image reconstruction"

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Xia, Weiyi, Ying Bi, Yu Cao, Kailong Xu, and Pengfei Fan. "Super-resolved image reconstruction by structured illumination microscopy." In Advanced Optical Imaging Technologies V, edited by P. Scott Carney, Xiao-Cong Yuan, and Kebin Shi. SPIE, 2023. http://dx.doi.org/10.1117/12.2642671.

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"LOR-based reconstruction for super-resolved 3D PET image." In 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). IEEE, 2013. http://dx.doi.org/10.1109/nssmic.2013.6829239.

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Levilly, Sebastien, Said Moussaoui, and Jean-Michel Serfaty. "Segmentation-Free Super-Resolved 4D flow MRI Reconstruction Exploiting Navier-Stokes Equations and Spatial Regularization." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897988.

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Zhang, Zhen, Yiteng Li, Marwah AlSinan, Xupeng He, Hyung Kwak, and Hussein Hoteit. "Multiscale Carbonate Rock Reconstruction Using a Hybrid WGAN-GP and Super-Resolution." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210461-ms.

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Abstract The X-ray micro-Computed Tomography (μ-CT) is the primary tool for digital rock imaging, which provides the foundation for numerically studying petrophysical properties of reservoir rocks at the pore scale. However, the finite resolution of μ-CT imaging cannot capture the micro-porosity at the sub-micrometer scale in carbonate rocks. The tradeoff between the resolution and field of view (FOV) is a persisting challenge in the industry. The machine-learning-based single-image super-resolution techniques has rapidly developed in the past few years. It is becoming a promising approach to "super-resolve" low-resolution carbonate rock images. In this study, we present a fast super-resolution generative adversarial network to enhance the image resolution of carbonate rocks. A pre-trained VGG network is implemented to extract important high-level features, from which the perceptual similarity is evaluated between the generated and ground-truth images. The novelty of this study is two-fold. First, the generator is significantly simplified with a fast super-resolution convolutional neural network. On the other hand, the spatial and channel squeeze-and excitation block is applied to recalibrate nonlinear feature mapping so that the quality of super-resolved images is promising even with much fewer residual blocks. To quantify the quality of the super-resolution images, we compare difference maps between the generated and ground-truth images. Numerical results indicate that the proposed network shows excellent potential in enhancing the resolution of heterogeneous carbonate rocks. In particular, the pixel errors are minor, and the super-resolution images exhibit clear and sharp edges and dissolved mineral texture. This study provides a novel machine-learning-based method using a simple generative adversarial network with squeeze and excitation blocks to super-resolve μ-CT images of carbonate rocks.
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Vo, Khoa D., and Len T. Bui. "StarSRGAN: Improving Real-World Blind Super-Resolution." In WSCG 2023 – 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. University of West Bohemia, Czech Republic, 2023. http://dx.doi.org/10.24132/csrn.3301.9.

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The aim of blind super-resolution (SR) in computer vision is to improve the resolution of an image without prior knowledge of the degradation process that caused the image to be low-resolution. The State of the Art (SOTA) model Real-ESRGAN has advanced perceptual loss and produced visually compelling outcomes using more complex degradation models to simulate real-world degradations. However, there is still room to improve the super-resolved quality of Real-ESRGAN by implementing recent techniques. This research paper introduces StarSRGAN, a novel GAN model designed for blind super-resolution tasks that utilize 5 various architectures. Our model provides new SOTA performance with roughly 10% better on the MANIQA and AHIQ measures, as demonstrated by experimental comparisons with Real-ESRGAN. In addition, as a compact version, StarSRGAN Lite provides approximately 7.5 times faster reconstruction speed (real-time upsampling from 540p to 4K) but can still keep nearly 90% of image quality, thereby facilitating the development of a real-time SR experience for future research.
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