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SSIM-based sparse image restoration
Journal of King Saud University: Computer and Information Sciences, 2022 In this paper, we provide a sparse image restoration algorithm with a SSIM-based objective function. The proposed technique is a modification to the SSIM-inspired OMP (iOMP) and, and it has two parallel sparse restoration paths.
A.N. Omara +3 more
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EAFormer: Edge-Aware Guided Adaptive Frequency-Navigator Network for Image Restoration. [PDF]
Sensors (Basel)Although many deep learning-based image restoration networks have emerged in various image restoration tasks, most can only perform well in a specific type of restoration task and still face challenges in the general performance of image restoration. The
Xie W, Zhou D, Zhang W, Wang W.
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Cellpose3: one-click image restoration for improved cellular segmentation. [PDF]
Nat MethodsGeneralist methods for cellular segmentation have good out-of-the-box performance on a variety of image types. However, existing methods struggle for images that are degraded by noise, blurred or undersampled, all of which are common in microscopy.
Stringer C, Pachitariu M.
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Zero-Shot Sand-Dust Image Restoration. [PDF]
Sensors (Basel)Natural sand-dust weather is complicated, and synthetic sand-dust datasets cannot accurately reflect the properties of real sand-dust images. Sand-dust image enhancement and restoration methods that are based on enhancement, on priors, or on data-driven ...
Shi F, Jia Z, Zhou Y.
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Vision Transformers in Image Restoration: A Survey. [PDF]
Sensors (Basel), 2023 The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks.
Ali AM +5 more
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Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration [PDF]
Trans. Mach. Learn. Res., 2023 Inversion by Direct Iteration (InDI) is a new formulation for supervised image restoration that avoids the so-called"regression to the mean"effect and produces more realistic and detailed images than existing regression-based methods.
M. Delbracio, P. Milanfar
semanticscholar +3 more sources
MemNet: A Persistent Memory Network for Image Restoration [PDF]
IEEE International Conference on Computer Vision, 2017 Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency problem is rarely realized for these very deep models, which results in the ...
Liu, Xiaoming +3 more
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Image restoration model for microscopic defocused images based on blurring kernel guidance. [PDF]
HeliyonDefocus blurring imaging seriously affects the observation accuracy and application range of optical microscopes, and the blurring kernel function is a key parameter for high-resolution image restoration.
Wei Y, Li Q, Hou W.
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An enhanced image restoration using deep learning and transformer based contextual optimization algorithm. [PDF]
Sci RepImage processing and restoration are important in computer vision, particularly for images that are damaged by noise, blur, and other issues. Traditional methods often have a hard time with problems like periodic noise and do not effectively combine ...
Senthil Anandhi A, Jaiganesh M.
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BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS [PDF]
Image Analysis and Stereology, 2011 In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the ...
Thordis Linda Thorarinsdottir
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