This repository is Tensorflow code for our paper entitled "Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion " . [Paper Download] [Project Website]
Network Architecture of the proposed CU-Net. For MIR related tasks, the final reconstruction (Point 4) is composed of the common reconstruction (Point 1) and the unique reconstruction (Point 2). For MIF related tasks, the final reconstruction is composed of the common reconstruction (Point 1) and the two unique reconstructions (Point 2 and Point 3).
If you find our work useful in your research or publications, please consider citing:
@inproceedings{Deng2019deep,
author = {Deng, Xin and Dragotti, Pier Luigi},
title = {Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)},
year= {2020}
}
Run the command "python train.py" in file "MIR_Task_pytorch" for multi-modal image restoration tasks, including RGB guided depth image SR,Flash gudied non-flash image denoising, etc.
Run the command "python MIF_train.py" in file "MIF_Task_pytorch"for multi-modal image fusion tasks, including multi-exposure image fusion, multi-focus image fusion, etc.
The testing datasets and our results in the paper can be downloaded from Googledrive.