Image Deconvolution
21 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Image Deconvolution
Stained mice brain blood vessels. Confocal-LFM
Most implemented papers
Modular proximal optimization for multidimensional total-variation regularization
We study \emph{TV regularization}, a widely used technique for eliciting structured sparsity.
Deep Blind Video Super-resolution
csbhr/Deep-Blind-VSR
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ICCV 2021
Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration.
A Framework for Fast Image Deconvolution with Incomplete Observations
In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
tum-vision/learn_prox_ops
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ICCV 2017
While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.
Blind Image Deconvolution using Deep Generative Priors
axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors
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This paper proposes a novel approach to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution (blind deblurring) using deep generative networks as priors.
Learning Deep Gradient Descent Optimization for Image Deconvolution
donggong1/learn-optimizer-rgdn
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Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.
Simultaneous Fidelity and Regularization Learning for Image Restoration
For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well.
Blind Image Deconvolution using Pretrained Generative Priors
axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors
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This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks.
Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
vpronina/DeepWienerRestoration
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ECCV 2020
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise.
