Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
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Updated
Oct 9, 2021 - MATLAB
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
SwinIR: Image Restoration Using Swin Transformer (official repository)
Collection of popular and reproducible image denoising works.
The state-of-the-art image restoration model without nonlinear activation functions.
[CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.
Learning Deep CNN Denoiser Prior for Image Restoration (CVPR, 2017) (Matlab)
A tensorflow implement of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising (TIP, 2018)
[CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for Image Restoration".
A Collection of Papers and Codes for CVPR2024/CVPR2021/CVPR2020 Low Level Vision
A Collection of Papers and Codes for ICCV2021 Low Level Vision and Image Generation
Code for "Toward Convolutional Blind Denoising of Real Photographs", CVPR 2019
[ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.
[CVPR 2020--Oral] CycleISP: Real Image Restoration via Improved Data Synthesis
The official implementation of IJCV & BMVC 2022 paper "One-Pot Multi-frame Denoising".
[ECCV] Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration. Advances in Image Manipulation (AIM) workshop ECCV 2022. Try it out! over 3.3M runs https://replicate.com/mv-lab/swin2sr
Artificial Intelligence Learning Notes.
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis (Machine Intelligence Research 2023)
Pytorch code for "Real image denoising with feature attention", ICCV (Oral), 2019.
[TPAMI 2022] Learning Enriched Features for Fast Image Restoration and Enhancement. Results on Defocus Deblurring, Denoising, Super-resolution, and image enhancement
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