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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 11–20 of 70 papers

TitleStatusHype
Plug-and-Play Posterior Sampling for Blind Inverse Problems—0
Frequency-domain Learning with Kernel Prior for Blind Image Deblurring—0
An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
Multi-scale Frequency Enhancement Network for Blind Image Deblurring—0
Self-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization—0
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser NetworkCode0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
Blind Image Deblurring with FFT-ReLU Sparsity PriorCode0
A Fast Blind Deblurring Algorithm Using Local Gradient Product PriorCode0
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