Image Deconvolution

11 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Modular proximal optimization for multidimensional total-variation regularization

albarji/proxTV 3 Nov 2014

We study \emph{TV regularization}, a widely used technique for eliciting structured sparsity.

Deep Blind Video Super-resolution

csbhr/Deep-Blind-VSR 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

alfaiate/DeconvolutionIncompleteObs 3 Feb 2016

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 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 12 Feb 2018

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 10 Apr 2018

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

csdwren/sfarl 12 Apr 2018

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.

Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

vpronina/DeepWienerRestoration 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.

Image Deconvolution via Noise-Tolerant Self-Supervised Inversion

royerlab/ssi-code 11 Jun 2020

We propose a general framework for solving inverse problems in the presence of noise that requires no signal prior, no noise estimate, and no clean training data.