Low-rank and spectral-spatial variation regularized hyperspectral image denoising algorithm

Yanhui Liu; Weiguo Wang

doi:10.1117/12.2679996

27 June 2023 Low-rank and spectral-spatial variation regularized hyperspectral image denoising algorithm

Yanhui Liu, Weiguo Wang

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270524 (2023) https://doi.org/10.1117/12.2679996
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Hyperspectral image (HSI) is an important carrier for recording, transmitting, and storing information. Denoising is an indispensable step in HSI processing, which is the process of restoring noisy images to high-quality images that can reflect the objective world. It is crucial to establish a reasonable denoising method. Most recent studies regard HSI as a three-dimensional tensor and then establish the low-rank method, or use the spatial information of the image to establish a total variation method. In this paper, we combine the spectral-spatial structure and low-rank properties of HSI to construct the regularization term and propose a low-rank spectral-spatial adaptive total variation (LRSSAHTV) model. Then we define a separable soft thresholding (SST) operator to minimize the spectral-spatial adaptive total variation (SSAHTV) regularization problem, which is the basic theory of our algorithm. A linearized alternating direction method of multipliers (LADMM) algorithm is proposed to solve the composite model. To verify the effectiveness of our method, we perform the simulated noisy HSI and the real polluted HSI denoising experiments in Section 4. We also compare the denoising effect of the proposed method with the total variation method and low-rank method, which shows that our method can obtain the most precise restored image.

Citation Download Citation

Yanhui Liu and Weiguo Wang "Low-rank and spectral-spatial variation regularized hyperspectral image denoising algorithm", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270524 (27 June 2023); https://doi.org/10.1117/12.2679996

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KEYWORDS

Denoising

Hyperspectral imaging

Matrices

Image denoising

Mathematical optimization

Image restoration

Algorithm development

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