Tensor-Based Sparse Representation for Hyperspectral Image Reconstruction Using RGB Inputs

Yingtao Duan, Nan Wang (), Yifan Zhang and Chao Song
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Yingtao Duan: School of Mathematics and Statistics, Hanshan Normal University, Chaozhou 521041, China
Nan Wang: School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Yifan Zhang: School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Chao Song: School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Mathematics, 2024, vol. 12, issue 5, 1-18

Abstract: Hyperspectral image (HSI) reconstruction from RGB input has drawn much attention recently and plays a crucial role in further vision tasks. However, current sparse coding algorithms often take each single pixel as the basic processing unit during the reconstruction process, which ignores the strong similarity and relation between adjacent pixels within an image or scene, leading to an inadequate learning of spectral and spatial features in the target hyperspectral domain. In this paper, a novel tensor-based sparse coding method is proposed to integrate both spectral and spatial information represented in tensor forms, which is capable of taking all the neighboring pixels into account during the spectral super-resolution (SSR) process without breaking the semantic structures, thus improving the accuracy of the final results. Specifically, the proposed method recovers the unknown HSI signals using sparse coding on the learned dictionary pairs. Firstly, the spatial information of pixels is used to constrain the sparse reconstruction process, which effectively improves the spectral reconstruction accuracy of pixels. In addition, the traditional two-dimensional dictionary learning is further extended to the tensor domain, by which the structure of inputs can be processed in a more flexible way, thus enhancing the spatial contextual relations. To this end, a rudimentary HSI estimation acquired in the sparse reconstruction stage is further enhanced by introducing the regression method, aiming to eliminate the spectral distortion to some extent. Abundant experiments are conducted on two public datasets, indicating the considerable availability of the proposed framework.

Keywords: spectral super-resolution; sparse representation; dictionary learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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