Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system

Yaniv Oiknine; Isaac Y. August; Liat Revah; Adrian Stern

doi:10.1117/12.2223647

4 May 2016 Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system

Yaniv Oiknine, Isaac Y. August, Liat Revah, Adrian Stern

Author Affiliations +

Proceedings Volume 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics; 985705 (2016) https://doi.org/10.1117/12.2223647
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

Recently we introduced a Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) system. The system is based on a single Liquid Crystal (LC) cell and a parallel sensor array where the liquid crystal cell performs spectral encoding. Within the framework of compressive sensing, the CS-MUSI system is able to reconstruct ultra-spectral cubes captured with only an amount of ~10% samples compared to a conventional system. Despite the compression, the technique is extremely complex computationally, because reconstruction of ultra-spectral images requires processing huge data cubes of Gigavoxel size. Fortunately, the computational effort can be alleviated by using separable operation. An additional way to reduce the reconstruction effort is to perform the reconstructions on patches. In this work, we consider processing on various patch shapes. We present an experimental comparison between various patch shapes chosen to process the ultra-spectral data captured with CS-MUSI system. The patches may be one dimensional (1D) for which the reconstruction is carried out spatially pixel-wise, or two dimensional (2D) - working on spatial rows/columns of the ultra-spectral cube, as well as three dimensional (3D).

Citation Download Citation

Yaniv Oiknine, Isaac Y. August, Liat Revah, and Adrian Stern "Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985705 (4 May 2016); https://doi.org/10.1117/12.2223647

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