Results 11 to 20 of about 298,685 (261)
Journal of Reconstructive Microsurgery Open, 2023 Objective We report the case of a 40-year-old man with an inveterate ulnar nerve neuroma following a laceration injury of his left wrist twenty-three years ago.
Laura A. Hruby +6 more
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Deep learning-based video stream reconstruction in mass-production diffractive optical systems [PDF]
Компьютерная оптика, 2021 Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision ...
V. Evdokimova +12 more
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Image reconstruction by linear programming [PDF]
IEEE Transactions on Image Processing, 2005 One way of image denoising is to project a noisy image to the subspace of admissible images derived, for instance, by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion.
Koji Tsuda, Gunnar Rätsch
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Image-to-Image MLP-mixer for Image Reconstruction
CoRR, 2022 Neural networks are highly effective tools for image reconstruction problems such as denoising and compressive sensing. To date, neural networks for image reconstruction are almost exclusively convolutional. The most popular architecture is the U-Net, a convolutional network with a multi-resolution architecture.
Youssef Mansour +2 more
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Bilevel Methods for Image Reconstruction
Foundations and Trends® in Signal Processing, 2022 This review discusses methods for learning parameters for image reconstruction problems using bilevel formulations.Image reconstruction typically involves optimizing a cost function to recover a vector of unknown variables that agrees with collected measurements and prior assumptions.
Caroline Crockett, Jeffrey A. Fessler
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Sensors, 2021 In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct
Karim Nagib +4 more
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Computational Imaging for VLBI Image Reconstruction [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 Accepted for publication at CVPR 2016, Project Website: http://vlbiimaging.csail.mit.edu/, Video of Oral Presentation at CVPR June 2016: https://www.youtube.com/watch?v ...
Katherine L. Bouman +5 more
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Lightweight Image Super-Resolution Reconstruction Based on Depthwise Separable Convolution [PDF]
Jisuanji gongcheng, 2022 Image super-resolution reconstruction aims to reconstruct a high-resolution image close to the real image according to the low-resolution image.The existing image super-resolution reconstruction methods based on the Convolutional Neural Network(CNN ...
LIU Cong, QU Dan, SI Nianwen, WEI Ziwei
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Stability of Image-Reconstruction Algorithms
IEEE Transactions on Computational Imaging, 2023 Robustness and stability of image-reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ($\ell_2$ and $\ell_1$ regularization) and present novel stability results for $\ell_p$-regularized linear inverse ...
Pol del Aguila Pla +2 more
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Metal Artifact Reduction in CT: Where Are We After Four Decades?
IEEE Access, 2016 Methods to overcome metal artifacts in computed tomography (CT) images have been researched and developed for nearly 40 years. When X-rays pass through a metal object, depending on its size and density, different physical effects will negatively affect ...
Lars Gjesteby +6 more
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