Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation

• URL: http://arxiv.org/abs/2407.06064v2
• Date: Mon, 9 Sep 2024 08:34:03 GMT
• Title: Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation
• Authors: Shuang Xu, Qiao Ke, Jiangjun Peng, Xiangyong Cao, Zixiang Zhao,
• Abstract summary: This paper introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed textitpan-denoising. Panchromatic (PAN) images capture similar structures and textures to HSIs but with less noise. Consequently, pan-denoising has the potential to uncover underlying structures and details beyond the internal information modeling of traditional HSI denoising methods. Experiments on synthetic and real-world datasets demonstrate that PWRCTV outperforms several state-of-the-art methods in terms of metrics and visual quality.
• Score: 20.240211073097758
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed \textit{pan-denoising}. In a given scene, panchromatic (PAN) images capture similar structures and textures to HSIs but with less noise. This enables the utilization of PAN images to guide the HSI denoising process. Consequently, pan-denoising, which incorporates an additional prior, has the potential to uncover underlying structures and details beyond the internal information modeling of traditional HSI denoising methods. However, the proper modeling of this additional prior poses a significant challenge. To alleviate this issue, the paper proposes a novel regularization term, Panchromatic Weighted Representation Coefficient Total Variation (PWRCTV). It employs the gradient maps of PAN images to automatically assign different weights of TV regularization for each pixel, resulting in larger weights for smooth areas and smaller weights for edges. This regularization forms the basis of a pan-denoising model, which is solved using the Alternating Direction Method of Multipliers. Extensive experiments on synthetic and real-world datasets demonstrate that PWRCTV outperforms several state-of-the-art methods in terms of metrics and visual quality. Furthermore, an HSI classification experiment confirms that PWRCTV, as a preprocessing method, can enhance the performance of downstream classification tasks. The code and data are available at https://github.com/shuangxu96/PWRCTV.

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