Hyperspectral Image Restoration Using 3-D Hybrid Higher Degree Total Variation Regularized Nonconvex Local Low-Rank Tensor Recovery

Zhou, Xinyu; Zhang, Ye; Liu, Jinhao; Zhao, Jun; Hu, Yue

doi:10.1109/lgrs.2022.3217581

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where X ∈ R M ×N ×B represents an HSI, and X (:, :, i) is its i-th band. This concept has been further extended, such as geometrical TV [35], [36], spatial-spectral TV (also known as cross TV) [37], higher-degree TV [38], nonconvex TV [39]- [41]. Besides these counterparts, weighted TV is also an important variant.…”

Section: B Internal Prior Modelingmentioning

confidence: 99%

Hyperspectral Image Denoising by Asymmetric Noise Modeling

Cao

Peng

et al. 2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

This paper 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 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 realworld 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.

show abstract