Deep learning training framework for image super resolution and restoration.
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Updated
Jul 9, 2024 - Python
Deep learning training framework for image super resolution and restoration.
A Collection of Papers and Codes for ECCV2024/ECCV2020 Low Level Vision
This is the Pytorch implementation of "OneRestore: A Universal Restoration Framework for Composite Degradation"
Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
A Collection of Low Level Vision Research Groups
neosr is a framework for training real-world single-image super-resolution networks.
[ECCV 2024] InstructIR: High-Quality Image Restoration Following Human Instructions https://huggingface.co/spaces/marcosv/InstructIR
Restoration for TEMPEST images using deep-learning
"Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement" (ICCV 2023) & (NTIRE 2024 Challenge)
The state-of-the-art image restoration model without nonlinear activation functions.
[ICCV 2023] Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution; runner-up method for the model complexity track in NTIRE2023 Efficient SR challenge
[ECCV 2024] Restoring Images in Adverse Weather Conditions via Histogram Transformer
Generate Images, Upscale Images, Fix Faces and Replace background using custom Stable DIffusion Models
A Collection of Papers and Codes for CVPR2024/CVPR2021/CVPR2020 Low Level Vision
[AAAI2024] Omni-Kernel Network for Image Restoration
[ICLR 2023] Selective Frequency Network for Image Restoration
[ICML2023] IRNeXt: Rethinking Convolutional Network Design for Image Restoration
Official implementation of the paper "DeblurDiNAT: A Lightweight and Effective Transformer for Image Deblurring".
[CVPR 2024] "CFAT: Unleashing Triangular Windows for Image Super-resolution"
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