Hyperspectral Image Super-Resolution With Optimized RGB Guidance

Ying Fu, Tao Zhang, Yinqiang Zheng, Debing Zhang, Hua Huang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11661-11670

Abstract


To overcome the limitations of existing hyperspectral cameras on spatial/temporal resolution, fusing a low resolution hyperspectral image (HSI) with a high resolution RGB (or multispectral) image into a high resolution HSI has been prevalent. Previous methods for this fusion task usually employ hand-crafted priors to model the underlying structure of the latent high resolution HSI, and the effect of the camera spectral response (CSR) of the RGB camera on super-resolution accuracy has rarely been investigated. In this paper, we first present a simple and efficient convolutional neural network (CNN) based method for HSI super-resolution in an unsupervised way, without any prior training. Later, we append a CSR optimization layer onto the HSI super-resolution network, either to automatically select the best CSR in a given CSR dataset, or to design the optimal CSR under some physical restrictions. Experimental results show our method outperforms the state-of-the-arts, and the CSR optimization can further boost the accuracy of HSI super-resolution.

Related Material


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[bibtex]
@InProceedings{Fu_2019_CVPR,
author = {Fu, Ying and Zhang, Tao and Zheng, Yinqiang and Zhang, Debing and Huang, Hua},
title = {Hyperspectral Image Super-Resolution With Optimized RGB Guidance},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}