pHSCNN: CNN-based hyperspectral recovery from a pair of RGB images
Year: 2022
Type: article
Abstract: To increase the fidelity of hyperspectral recovery from RGB images, we propose a pairwise-image-based hyperspectral convolutional neural network (pHSCNN) to recover hyperspectral images from a pair of RGB images, obtained by the same color sensor with and without an optical filter in front of the imaging lens. The proposed method avoids the pitfall of requiring multiple color sensors to obtain different RGB images and achieves higher accuracy than recovery from single RGB image. Besides, pHSCNN can also optimize the optical filter to further improve the performance. To experiment on real data, we built a dual-camera hyperspectral imaging system and created a real-captured hyperspectral-RGB dataset. Experimental results demonstrate the superiority of pHSCNN with the highest accuracy of the recovered hyperspectral signature perceptually and numerically.
Source: Optics Express
DOI: 10.1364/oe.461383
Authors: Yuanyuan Sun, Junchao Zhang, Rongguang Liang
Institutions: University of Arizona, Central South University
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,270
OpenAlex ID: W4282914872