Image Deconvolution
22 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
Latest papers
Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images
A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation's solutions of quantum physics.
Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
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.
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.
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.
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.
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.
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.
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 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.
