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

Shuang Xu, Qiao Ke, Jiangjun Peng, Xiangyong Cao, Zixiang Zhao

科研成果: 期刊稿件文章同行评审

7 引用 (Scopus)

摘要

This article introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed 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 article 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 total variation (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 (ADMM). 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.

源语言英语
文章编号5528714
期刊IEEE Transactions on Geoscience and Remote Sensing
62
DOI
出版状态已出版 - 2024

指纹

探究 'Pan-Denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation' 的科研主题。它们共同构成独一无二的指纹。

引用此