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
Latest papers
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.
Estimation of motion blur kernel parameters using regression convolutional neural networks
duckduckpig/regression_blur
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Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel.
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.
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.
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.
Explore Image Deblurring via Encoded Blur Kernel Space
VinAIResearch/blur-kernel-space-exploring
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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.
Explore Image Deblurring via Blur Kernel Space
VinAIResearch/blur-kernel-space-exploring
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This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.
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.
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.
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.
