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Hyperspectral light field image denoising
Author(s): Yun Liu; Na Qi; Zhen Cheng; Dong Liu; Zhiwei Xiong
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Paper Abstract

Hyperspectral light field (HSLF) images with enriched spectral and angular information provide better representation of real scenes than conventional 2D images. In this paper, we propose a novel denoising method for HSLF images. The proposed method consists of two main steps. First, we generalize the intrinsic tensor sparsity (ITS) measure previously used for 3D hyperspectral image denoising to the 5D HSLF, by using the global correlation along the spectral dimension and the nonlocal similarity across the spatial and angular dimensions. Second, we further exploit the spatial-angular correlation by integrating light field super-resolution (SR) into the denoising process. In this way, the 5D HSLF can be better recovered. Experimental results validate the superior performance of the proposed method in terms of both objective and subjective quality on a self-collected HSLF dataset, in comparison with directly applying the state-of-the-art denoising methods.

Paper Details

Date Published: 12 January 2018
PDF: 8 pages
Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106201E (12 January 2018); doi: 10.1117/12.2295065
Show Author Affiliations
Yun Liu, Univ. of Science and Technology of China (China)
Na Qi, Beijing Univ. of Technology (China)
Zhen Cheng, Univ. of Science and Technology of China (China)
Dong Liu, Univ. of Science and Technology of China (China)
Zhiwei Xiong, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10620:
2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology
Guohai Situ; Xun Cao; Wolfgang Osten; Liquan Dong, Editor(s)

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