Blind Image Deblurring
14 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
Benchmarks
These leaderboards are used to track progress in Blind Image Deblurring
Most implemented papers
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
Convolutional neural networks excel in a number of computer vision tasks.
Learning Deep Gradient Descent Optimization for Image Deconvolution
donggong1/learn-optimizer-rgdn
•
•
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
•
•
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
•
•
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
Then, a novel algorithm is designed to efficiently exploit the sparsity of PMP in deblurring.
Raw Image Deblurring
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
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
•
•
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.
