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Blind Image Deblurring

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

Papers

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Showing 1–50 of 70 papers

TitleStatusHype
Blind Image Deblurring with Unknown Kernel Size and Substantial NoiseCode4
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion RestorationCode1
End-to-end Interpretable Learning of Non-blind Image DeblurringCode1
INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light ConditionsCode1
Raw Image DeblurringCode1
Explore Image Deblurring via Blur Kernel SpaceCode1
Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image DeblurringCode1
Explore Image Deblurring via Encoded Blur Kernel SpaceCode1
Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolutionCode1
A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image DeblurringCode1
Estimation of motion blur kernel parameters using regression convolutional neural networksCode0
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural NetworksCode0
DWDN: Deep Wiener Deconvolution Network for Non-Blind Image DeblurringCode0
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser NetworkCode0
Blind Image Deblurring with FFT-ReLU Sparsity PriorCode0
Efficient Blind Deblurring under High Noise LevelsCode0
A Fast Blind Deblurring Algorithm Using Local Gradient Product PriorCode0
Learning Deep Gradient Descent Optimization for Image DeconvolutionCode0
Blind Image Deconvolution using Pretrained Generative PriorsCode0
Deblurring using Analysis-Synthesis Networks Pair—0
DELAD: Deep Landweber-guided deconvolution with Hessian and sparse prior—0
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
Frequency-domain Learning with Kernel Prior for Blind Image Deblurring—0
Graph-Based Blind Image Deblurring From a Single Photograph—0
Image Restoration from Parametric Transformations using Generative Models—0
Kernel Estimation from Salient Structure for Robust Motion Deblurring—0
Learning a Discriminative Prior for Blind Image Deblurring—0
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond—0
Learning Discriminative Data Fitting Functions for Blind Image Deblurring—0
Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring—0
Multi-scale Frequency Enhancement Network for Blind Image Deblurring—0
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images—0
Noise-Blind Image Deblurring—0
Non-Uniform Blind Deblurring by Reblurring—0
Non-uniform Motion Deblurring with Blurry Component Divided Guidance—0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
OID: Outlier Identifying and Discarding in Blind Image Deblurring—0
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring—0
Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring—0
Plug-and-Play Posterior Sampling for Blind Inverse Problems—0
Point spread function estimation for blind image deblurring problems based on framelet transform—0
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems—0
Scale Adaptive Blind Deblurring—0
Select Good Regions for Deblurring based on Convolutional Neural Networks—0
Self-Paced Kernel Estimation for Robust Blind Image Deblurring—0
Self-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization—0
Semi-Blind Image Deblurring Based on Framelet Prior—0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
Unsupervised Blind Image Deblurring Based on Self-Enhancement—0
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