{"repo":"mv-lab/AISP","free":true,"listed":false,"github":"https://github.com/mv-lab/AISP","clone":"git clone https://github.com/mv-lab/AISP.git","description":"AI Image Signal Processing and Computational Photography. Official library for NTIRE (CVPR) and AIM (ICCV/ECCV) Challenges. You will find Learned ISPs, RAW Restoration-Upsampling-Reconstruction, Image Enhancement, Bokeh rendering and more!","language":"Jupyter Notebook","stars":592,"topics":["computational-photography","computer-vision","deep-learning","image-processing","inverse-problems","isp","low-level-vision","mobile-ai","ntire","deblurring"],"license":null,"category":"machine-learning","readme_excerpt":"AI Image Signal Processing and Computational Photography Deep learning for low-level computer vision and imaging Marcos V. Conde, Radu Timofte Computer Vision Lab, CAIDAS, University of Würzburg --------------------------------------------------- Topics This repository contains material for RAW image processing, RAW Restoration and Super-Resolution, RAW reconstruction from sRGB, learned Image Signal Processing (ISP), Image Enhancement and Restoration (denoising, deblurring), Bokeh rendering, and much more! 📷 NEWS 🚀🚀 - NTIRE (New Trends in Image Restoration and Enhancement) workshop at CVPR 2025 - 🔥 This repo is back and will be updated with many more works! ---- RAW Image Restoration, Super-Resolution and Reconstruction at NTIRE CVPR 2025 We provide code for the following challenges: - NTIRE 2025 RAW Restoration Challenge: Track1) Super-Resolution - NTIRE 2025 RAW Restoration Challenge: Track2) Restoration - NTIRE 2025 RAW Image Reconstruction from sRGB Check the tutorial for generating degraded RAW images! You can learn: how to read and visualize RAWs, add noise, blur, and downsample. Base code for training RAW restoration, denoising and super-resolution methods. The tutorial is at imresutils/generate lq.ipynb. ---- Official repository for the following works: Most recent works: - BSRAW: Improving Blind RAW Image Super-Resolution, WACV 2024 - Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey, CVPRW 2024 - Toward Efficient Deep Blind Raw Image Restoration, IC","default_branch":null,"files":null,"tree":[],"storefront":"/r/mv-lab","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/mv-lab/AISP/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}