Open Access3D denoised completion network for deep single-pixel reconstruction of hyperspectral images
Author(s) -
Valeriya Pronina,
Antonio Lorente Mur,
Juan F P J Abascal,
Françoise Peyrin,
Dmitry V. Dylov,
Nicolas Ducros
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.443134
Subject(s) - hyperspectral imaging , computer science , artificial intelligence , pixel , redundancy (engineering) , iterative reconstruction , computer vision , underdetermined system , detector , tikhonov regularization , regularization (linguistics) , convolution (computer science) , inverse problem , pattern recognition (psychology) , algorithm , mathematics , artificial neural network , telecommunications , mathematical analysis , operating system
Single-pixel imaging acquires an image by measuring its coefficients in a transform domain, thanks to a spatial light modulator. However, as measurements are sequential, only a few coefficients can be measured in the real-time applications. Therefore, single-pixel reconstruction is usually an underdetermined inverse problem that requires regularization to obtain an appropriate solution. Combined with a spectral detector, the concept of single-pixel imaging allows for hyperspectral imaging. While each channel can be reconstructed independently, we propose to exploit the spectral redundancy between channels to regularize the reconstruction problem. In particular, we introduce a denoised completion network that includes 3D convolution filters. Contrary to black-box approaches, our network combines the classical Tikhonov theory with the deep learning methodology, leading to an explainable network. Considering both simulated and experimental data, we demonstrate that the proposed approach yields hyperspectral images with higher quantitative metrics than the approaches developed for grayscale images.