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Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization

Research paper by Shiying He, Haiwei Zhou, Yao Wang, Wenfei Cao, Zhi Han

Indexed on: 23 Jan '16Published on: 23 Jan '16Published in: Computer Science - Computer Vision and Pattern Recognition



Abstract

In this paper, we propose a novel approach to hyperspectral image super-resolution by modeling the global spatial-and-spectral correlation and local smoothness properties over hyperspectral images. Specifically, we utilize the tensor nuclear norm and tensor folded-concave penalty functions to describe the global spatial-and-spectral correlation hidden in hyperspectral images, and 3D total variation (TV) to characterize the local spatial-and-spectral smoothness across all hyperspectral bands. Then, we develop an efficient algorithm for solving the resulting optimization problem by combing the local linear approximation (LLA) strategy and alternative direction method of multipliers (ADMM). Experimental results on one hyperspectral image dataset illustrate the merits of the proposed approach.