  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.
