Blind Image Deblurring

15 papers with code • 0 benchmarks • 0 datasets

Blind Image Deblurring is a classical problem in image processing and computer vision, which aims to recover a latent image from a blurred input.

Source: Learning a Discriminative Prior for Blind Image Deblurring

Most implemented papers

Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks

skokec/DAU-ConvNet 20 Feb 2019

Convolutional neural networks excel in a number of computer vision tasks.

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.

Efficient Blind Deblurring under High Noise Levels

kidanger/high-noise-deblurring 19 Apr 2019

In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency.

Blind Image Deconvolution using Pretrained Generative Priors

axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors 20 Aug 2019

This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks.

End-to-end Interpretable Learning of Non-blind Image Deblurring

teboli/CPCR ECCV 2020

Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates.

A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring

FWen/deblur-pmp 29 Oct 2020

Then, a novel algorithm is designed to efficiently exploit the sparsity of PMP in deblurring.

Raw Image Deblurring

bob831009/raw_image_deblurring 8 Dec 2020

Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images.

Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

jdong/dwdn NeurIPS 2020

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning.

Explore Image Deblurring via Blur Kernel Space

VinAIResearch/blur-kernel-space-exploring 1 Apr 2021

This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.

Explore Image Deblurring via Encoded Blur Kernel Space

VinAIResearch/blur-kernel-space-exploring CVPR 2021

This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.