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
INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions
zhihongz/infwide
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In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and integrates the two complementary outputs with a subtle multi-scale fusion network for high quality night photograph deblurring.
Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
subeeshvasu/Awesome-Deblurring
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Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields.
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
sony/gibbsddrm
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Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution
claroche-r/fastdiffusionem
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Our method alternates between approximating the expected log-likelihood of the inverse problem using samples drawn from a diffusion model and a maximization step to estimate unknown model parameters.
