SwinIR: Image Restoration Using Swin Transformer (official repository)
The state-of-the-art image restoration model without nonlinear activation functions.
Collection of popular and reproducible image denoising works.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
A Collection of Papers and Codes for CVPR2025/CVPR2024/CVPR2021/CVPR2020 Low Level Vision
[CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.
The official implementation of IJCV & BMVC 2022 paper "One-Pot Multi-frame Denoising".
[CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for Image Restoration".
[ICLR 2024] Controlling Vision-Language Models for Universal Image Restoration. 5th place in the NTIRE 2024 Restore Any Image Model in the Wild Challenge.
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis (Machine Intelligence Research 2023)
🔥 🔥 🔥 A paper list of some recent Computer Vision(CV) works
[ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.
[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
Learning Deep CNN Denoiser Prior for Image Restoration (CVPR, 2017) (Matlab)
[CVPR 2020--Oral] CycleISP: Real Image Restoration via Improved Data Synthesis
Code for "Toward Convolutional Blind Denoising of Real Photographs", CVPR 2019
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising (TIP, 2018)
Official Code for ICCV 2021 paper "Towards Flexible Blind JPEG Artifacts Removal (FBCNN)"
[TPAMI 2022] Learning Enriched Features for Fast Image Restoration and Enhancement. Results on Defocus Deblurring, Denoising, Super-resolution, and image enhancement
PromptIR: Prompting for All-in-One Blind Image Restoration [NeurIPS 2023]
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