Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Published in Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 2018
Abstract
Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral superresolution aims at producing a high-resolution hyperspectral image from the RGB observation with super-resolution in spectral domain. However, it is challenging to accurately reconstruct a high-dimensional continuous spectrum from three discrete intensity values at each pixel, since too much information is lost during the procedure where the latent hyperspectral image is downsampled (e.g., with ×10 scaling factor) in spectral domain to produce an RGB observation. To address this problem, we present a multi-scale deep convolutional neural network (CNN) to explicitly map the input RGB image into a hyperspectral image. Through symmetrically downsampling and upsampling the intermediate feature maps in a cascading paradigm, the local and non-local image information can be jointly encoded for spectral representation, ultimately improving the spectral reconstruction accuracy. Extensive experiments on a large hyperspectral dataset demonstrate the effectiveness of the proposed method.
Citation
@inproceedings{yan2018accurate,
title={Accurate Spectral Super-Resolution from Single RGB Image Using Multi-scale CNN},
author={Yan, Yiqi and Zhang, Lei and Li, Jun and Wei, Wei and Zhang, Yanning},
booktitle={Chinese Conference on Pattern Recognition and Computer Vision (PRCV)},
pages={206–217},
year={2018},
organization={Springer}
}