【TIM'2023🔥】Multi-scale Synergism Ensemble Progressive and Contrastive Investigation for Image Restoration

Welcome! This is the official implementation of our paper: Multi-scale Synergism Ensemble Progressive and Contrastive Investigation for Image Restoration, published in IEEE Transactions on Instrumentation and Measurement.

Authors: Zhiying Jiang†, Shuzhou Yang†, Jinyuan Liu, Xin Fan, Risheng Liu* (†equal contribution, *corresponding author)

Prerequisites

• Linux or macOS
• Python 3.7
• NVIDIA GPU + CUDA CuDNN

🔑 Setup

Type the command:

conda create -n SEPC python=3.7
conda activate SEPC
pip install -r requirements.txt
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch

🧩 Download

You need create a directory ./logs/[YOUR-MODEL] (e.g., ./logs/SEPC_derainL).
Download the pre-trained model and put it into ./logs/[YOUR-MODEL].
Here we release the pre-trained model trained on Rain100L and Rain100H:

• Model(Rain100L)
• Model(Rain100H)

🚀 Quick Run

• You need create a directory ./testData and put the degraded images to it.
• Test the model with the pre-trained weights:

CUDA_VISIBLE_DEVICES=0 python test.py

• The test results will be saved to a directory here: ./results.

🤖 Training

• You need create a directory ./trainData and put the degraded training data to it.
• Train a model:

CUDA_VISIBLE_DEVICES=0 python train.py

• Loss curve and checkpoint can be found in the directory ./log.

📌 Citation

If you find this code useful for your research, please use the following BibTeX entry.

@ARTICLE{10363208,
  author={Jiang, Zhiying and Yang, Shuzhou and Liu, Jinyuan and Fan, Xin and Liu, Risheng},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={Multiscale Synergism Ensemble Progressive and Contrastive Investigation for Image Restoration}, 
  year={2024},
  volume={73},
  number={},
  pages={1-14},
  doi={10.1109/TIM.2023.3343823}}