The official code of the IEEE Access paper Multiple Adverse Weather Removal Using Masked-Based Pre-Training and Dual-Pooling Adaptive Convolution (MPDAC)
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Updated
Jun 1, 2024 - Jupyter Notebook
The official code of the IEEE Access paper Multiple Adverse Weather Removal Using Masked-Based Pre-Training and Dual-Pooling Adaptive Convolution (MPDAC)
This paper is accepted by IEEE TCSVT
Inference code for "Unified Multi-Weather Transformer for Multi-Weather Image Restoration".
Source Code for WeatherRemover
This is the source code of PMS-Net: Robust Haze Removal Based on Patch Map for Single Images which has been published in CVPR 2019 Long Beach
This is the project page of our paper which has been published in ECCV 2020.
This is the source code of PMHLD-Patch-Map-Based-Hybrid-Learning-DehazeNet-for-Single-Image-Haze-Removal which has been accepted by IEEE Transaction on Image Processing 2020.
[ECCV 2024] OneRestore: A Universal Restoration Framework for Composite Degradation
This paper is accepted by ICCV 2021.
[ICCV 2023] Snow Removal in Video: A New Dataset and A Novel Method
[ECCV 2024] Histoformer: Restoring Images in Adverse Weather Conditions via Histogram Transformer
Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN). ECCV, 2022.
[CVPR 2022] Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model
Pytorch Code for the paper TransWeather - CVPR 2022
Code for "Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models" [TPAMI 2023]
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