Deep learning training framework for image super resolution and restoration.
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Updated
Sep 15, 2024 - Python
Deep learning training framework for image super resolution and restoration.
Collection of recent shadow removal works, including papers, codes, datasets, and metrics.
[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] OneRestore: A Universal Restoration Framework for Composite Degradation
[ICCV'23] Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks
“Disparitybased space-variant image deblurring,” Signal Processing: Image Communication, vol. 28, no. 7, pp. 792–808, 2013.
A Collection of Papers and Codes for ECCV2024/ECCV2020 Low Level Vision
[ICME 2023 Oral Presentation]
LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement
Official implementation of the paper "DeblurDiNAT: A Generalizable Transformer for Perceptual Image Deblurring".
The Official Implementation for "HAIR: Hypernetworks-based All-in-One Image Restoration".
A Collection of Papers and Codes for CVPR2024/CVPR2021/CVPR2020 Low Level Vision
neosr is a framework for training real-world single-image super-resolution networks.
[ECCV 2024] Histoformer: Restoring Images in Adverse Weather Conditions via Histogram Transformer
DGNL-Net and RainCityscapes
This is the official PyTorch implementation of ShadowRefiner. Our method is winner of Perceptual Track and achieves the second-best performance for Fidelity Track in NTIRE 2024 Shadow Removal Challenge (CVPR 2024 Workshop)
A Mountain-Shaped Single-Stage Network for Accurate Image Restoration
A curated list of research papers and datasets related to image and video deblurring.
A goal-oriented X-ray restoration approach with Restore-to-Classify GANs.
The official implementation for IEEE-ICASSP 2024 paper "Flare-Free Vision: Empowering Uformer with Depth Insights"
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