lronkitty/NSSNN

Nonlocal Spatial-Spectral Neural Network for Hyperspectral Image Denoising (NSSNN)

NSSNN

The implementation of TGRS 2022 paper "Nonlocal Spatial-Spectral Neural Network for Hyperspectral Image Denoising"

Requisites

• See torch_37.yaml

Quick Start

1. Preparing your training/testing datasets

• Download HSIs from here.

Training dataset

• Create training datasets by python utility/lmdb_data.py

Testing dataset

Note matlab is required to execute the following instructions.

• You can use the testing set we prepared for you in datasets/test/

• Read the matlab code of matlab/generate_dataset* to understand how we generate noisy HSIs.

• Read and modify the matlab code of matlab/HSIData.m to generate your own testing dataset

2. Testing with pretrained models

• Our pretrained models are in checkpoints/, you can use the scripts eval*.sh to test the pretrained models.

3. Training from scratch

• Use training scipts train*.sh to train your own models.

Citation

If you find this work useful for your research, please cite:

@ARTICLE{fu2022nssnn,
  author={Fu, Guanyiman and Xiong, Fengchao and Lu, Jianfeng and Zhou, Jun and Qian, Yuntao},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Nonlocal Spatial–Spectral Neural Network for Hyperspectral Image Denoising}, 
  year={2022},
  volume={60},
  number={},
  pages={1-16},
  doi={10.1109/TGRS.2022.3217097}}

## Contact
Please contact me if there is any question (gym.fu@njust.edu.cn)