SwinIR: Image Restoration Using Swin Transformer (official repository)
-
Updated
May 14, 2024 - Python
SwinIR: Image Restoration Using Swin Transformer (official repository)
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.
A tensorflow implement of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
[CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for Image Restoration".
[ECCV 2020] Learning Enriched Features for Real Image Restoration and Enhancement. SOTA results for image denoising, super-resolution, and image enhancement.
Code for "Toward Convolutional Blind Denoising of Real Photographs", CVPR 2019
The official implementation of IJCV & BMVC 2022 paper "One-Pot Multi-frame Denoising".
[CVPR 2020--Oral] CycleISP: Real Image Restoration via Improved Data Synthesis
[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
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis (Machine Intelligence Research 2023)
Artificial Intelligence Learning Notes.
[TPAMI 2022] Learning Enriched Features for Fast Image Restoration and Enhancement. Results on Defocus Deblurring, Denoising, Super-resolution, and image enhancement
Pytorch code for "Real image denoising with feature attention", ICCV (Oral), 2019.
Official Code for ICCV 2021 paper "Towards Flexible Blind JPEG Artifacts Removal (FBCNN)"
Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
PyTorch Implementation of image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections (NIPS 2016)
Image denoising using deep CNN with batch renormalization(Neural Networks,2020)
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. ⭐⭐⭐⭐⭐⭐
[ICLR 2024] Controlling Vision-Language Models for Universal Image Restoration. 5th place in the NTIRE 2024 Restore Any Image Model in the Wild Challenge.
Add a description, image, and links to the image-denoising topic page so that developers can more easily learn about it.
To associate your repository with the image-denoising topic, visit your repo's landing page and select "manage topics."