Hyperspectral Image Super-resolution via Multi-stage Scheme without Employing Spatial Degradation

This is a Model-based algorithm for MSI-HSI fusion without employing spatial degradation. HyMS achieves fusion super-resolution in a short time with SOTA performance.
An online version based on AI-Studio is released: >>>> Online Version(CAVE/ Chikusei)<<<<

Note

GPU-based HyMS may makes mistakes result from a wrong package config. The CPU-based one is the stable version.

Flowchart

Abstract

Recently, it has become popular to obtain a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution RGB image (HR-RGB). Existing HSI super-resolution methods are designed based on a known spatial degeneration. In practice, it is difficult to obtain correct spatial degradation, which restricts the performance of existing methods. Therefore, we propose a multi-stage scheme without employing the spatial degradation model. The multi-stage scheme consists of three stages: initialization, modification, and refinement. According to the angle similarity between the HR-RGB pixel and LR-HSI spectra, we first initialize a spectrum for each HR-RGB pixel. Then, we propose a polynomial function to modify the initialized spectrum so that the RGB color values of the modified spectrum are the same as the HR-RGB. Finally, the modified HR-HSI is refined by a proposed optimization model, in which a novel, to the best of our knowledge, spectral-spatial total variation (SSTV) regularizer is investigated to keep the spectral and spatial structure of the reconstructed HR-HSI. The experimental results on two public datasets and our real-world images demonstrate our method outperforms eight state-of-the-art existing methods in terms of both reconstruction accuracy and computational efficiency.

Result presentation

Guidance

Add your dataset path in config.py
Run main.py

Running interface

Requirements

Environment

Python 3.8
Numba, Cupy
Numpy, Scipy, Opencv

Datasets

CAVE dataset, Preprocessed CAVE dataset.

Contact

For any questions, feel free to email Xuheng Cao(📧[email protected]).
If you find our work useful in your research, please cite our paper 🙂

@article{article,
author = {Cao, Xuheng and Lian, Yusheng and Liu, Zilong and Zhou, Han and Bin, Wang and Zhang, Wan and Huang, Beiqing},
year = {2022},
month = {09},
pages = {5184-5187},
title = {Hyperspectral Image Super-resolution via Multi-stage Scheme without Employing Spatial Degradation},
journal = {Optics Letters},
doi = {10.1364/OL.473020}
}