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Proceedings Paper

Blind noise parameters estimation for multichannel images using deep convolutional neural networks
Author(s): M. Uss; B. Vozel; V. Lukin; K. Chehdi
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Paper Abstract

This paper investigates the problem of blind noise parameters estimation (BNPE) of multispectral/hyperspectral images (M/HSI) using deep convolutional neural networks. In contrast to single-band images, M/HSI possess property of interband correlation that can effectively improve quality and accuracy of BNPE. Therefore, in this work, we extend a previously proposed CNN for BNPE of single-band images, called NoiseNet, to vector case. The proposed vNoiseNet Convolutional Neural Network (CNN) can be applied to three-band images including RGB images from Digital Single- Lens Reflex (DSLR) cameras, and subsets of M/HSI bands. Training data for the proposed CNN were obtained from three sources: calibrated images captured by DSLR Nikon D80 camera, AVIRIS data with accurately estimated noise parameters, and Sentinel-2 data with synthetic noise. The vNoiseNet estimates both sub-band (component) image noise variance and uncertainty of this estimate from 32×32×3 image patches. On the basis of a set of such estimates, both signal-independent (SI) and signal-dependent (SD) noise component parameters can be robustly estimated. Experiments on NED2012 database, AVIRIS data and Sentinel-2 data demonstrate high accuracy of the proposed CNN.

Paper Details

Date Published: 7 October 2019
PDF: 11 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115514 (7 October 2019); doi: 10.1117/12.2533300
Show Author Affiliations
M. Uss, National Aerospace Univ. (Ukraine)
B. Vozel, Univ. de Rennes 1 (France)
V. Lukin, National Aerospace Univ. (Ukraine)
K. Chehdi, Univ. de Rennes 1 (France)

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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