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Regularization Parameter Estimation for non-negative hyperspectral image deconvolution

IEEE TRANSACTIONS ON image PROCESSING 2016年第11期25卷 5316-5330页

作者： Song, Yingying Brie, David Djermoune, El-Hadi Henrot, Simon Univ Lorraine Ctr Rech Automat Nancy F-54506 Vandoeuvre Les Nancy France Univ Lorraine Ctr Natl Rech Sci F-54506 Vandoeuvre Les Nancy France Signal & Image Dept Grenoble Images Parole Signal Automat Lab F-38402 St Martin Dheres France

This paper aims at studying a method to automatically estimate the regularization parameters of non-negative hyperspectral image deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) and the maximum curvature criterion (MCC) are proposed to estimate regularization parameters especially for the non-negativity constrained deconvolution problem. MDC has good theoretical properties (convexity and uniqueness) but requires to choose a reference point. On the contrary, MCC does not need to choose any reference point but does not have interesting theoretical properties. A grid-search-based approach to minimize the computational cost of MDC and MCC is proposed. It results in fast approaches to estimate the regularization parameters. Based on simulated 2D images, the proposed approaches are compared with the state-of-the-art methods, confirming the effectiveness of the MDC and MCC for the non-negativity constrained image deconvolution problem. In the case of non-negative hyperpsectral image deconvolution, the fast MDC yields better performances than the fast MCC. An application to real-world hyperspectral fluorescence microscopy images is also provided;it confirms the superiority of MDC.

关键词： non-negative hyperspectral image deconvolution multi-objective optimization regularization parameter estimation

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Spatial resolution improvement of hyperspectral images by deconvolution and joint unmixing-deconvolution

Spatial resolution improvement of hyperspectral images by de...

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作者： Yingying Song Universite de Lorraine

学位级别：博士

A hyperspectral image is a 3D data cube in which every pixel provides local spectral information about a scene of interest across a large number of contiguous bands. The observed images may suﬀer from degradation due to the measuring device, resulting in a convolution or blurring of the images. hyperspectral image deconvolution (HID) consists in removing the blurring to improve the spatial resolution of images at best. A Tikhonov-like HID criterion with non-negativity constraint is consid- ered here. This method considers separable spatial and spectral regularization terms whose strength are controlled by two regularization parameters. First part of this thesis proposes the maximum curvature criterion (MCC) and the minimum distance criterion (MDC) to automatically estimate these regularization parameters by formulating the deconvolution problem as a multi-objective opti- mization problem. The second part of this thesis proposes the sliding block regularized (SBR-LMS) algorithm for the online deconvolution of hypserspectral images as provided by whiskbroom and pushbroom scanning systems. The proposed algorithm accounts for the convolution kernel non- causality and including non-quadratic regularization terms while maintaining a linear complexity compatible with real-time processing in industrial applications. The third part of this thesis pro- poses joint unmixing-deconvolution methods based on the Tikhonov criterion in both oﬄine and online contexts. The non-negativity constraint is added to improve their performances.

关键词： non-negative hyperspectral image deconvolution multi-objective optimization regularization parameter estimation online deconvolution zero-attracting LMS joint unmixing and deconvolution

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