A curated list of resources for Image and Video Deblurring
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Updated
May 3, 2024
A curated list of resources for Image and Video Deblurring
The state-of-the-art image restoration model without nonlinear activation functions.
[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.
[CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.
A Collection of Papers and Codes for CVPR2024/CVPR2021/CVPR2020 Low Level Vision
[CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for Image Restoration".
Learning Deep CNN Denoiser Prior for Image Restoration (CVPR, 2017) (Matlab)
Keras implementation of "DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks"
[ICLR 2024] Controlling Vision-Language Models for Universal Image Restoration. 5th place in the NTIRE 2024 Restore Any Image Model in the Wild Challenge.
Simple framework for image and video deblurring, implemented by PyTorch
A Flexible and Unified Image Restoration Framework (PyTorch), including state-of-the-art image restoration model. Such as NAFNet, Restormer, MPRNet, MIMO-UNet, SCUNet, SwinIR, HINet, etc. ⭐⭐⭐⭐⭐⭐
A Collection of Papers and Codes for ICCV2021 Low Level Vision and Image Generation
[ECCV 2022] LEDNet: Joint Low-light Enhancement and Deblurring in the Dark
KBNet: Kernel Basis Network for Image Restoration
This is a survey that reviews deep learning models and benchmark datasets related to blind motion deblurring and provides a comprehensive evaluation of these models.
A Collection of Papers and Codes for ECCV2020 Low Level Vision or Image Reconstruction
Code for paper: Memory Augment is All Your Need for image restoration(cloud,rain,shadow removal, low-light image enhancement, image deblur)即插即用提点的记忆模块
A Collection of Low Level Vision Research Groups
MATLAB package of iterative regularization methods and large-scale test problems. This software is described in the paper "IR Tools: A MATLAB Package of Iterative Regularization Methods and Large-Scale Test Problems" that will be published in Numerical Algorithms, 2018.
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