Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image

TL;DR

Hipandas is a novel joint learning framework that simultaneously denoises and super-resolves hyperspectral images by fusing panchromatic images, outperforming existing methods in accuracy and visual quality.

Contribution

This paper introduces Hipandas, a zero-shot joint denoising and super-resolution method for hyperspectral images that leverages image fusion with panchromatic images and a two-stage training strategy.

Findings

Outperforms state-of-the-art algorithms on simulated datasets.

Produces more accurate high-resolution hyperspectral images.

Generates visually pleasing results in real-world scenarios.

Abstract

Hyperspectral images (HSIs) are frequently noisy and of low resolution due to the constraints of imaging devices. Recently launched satellites can concurrently acquire HSIs and panchromatic (PAN) images, enabling the restoration of HSIs to generate clean and high-resolution imagery through fusing PAN images for denoising and super-resolution. However, previous studies treated these two tasks as independent processes, resulting in accumulated errors. This paper introduces \textbf{H}yperspectral \textbf{I}mage Joint \textbf{Pand}enoising \textbf{a}nd Pan\textbf{s}harpening (Hipandas), a novel learning paradigm that reconstructs HRHS images from noisy low-resolution HSIs (LRHS) and high-resolution PAN images. The proposed zero-shot Hipandas framework consists of a guided denoising network, a guided super-resolution network, and a PAN reconstruction network, utilizing an HSI low-rank prior and a newly introduced detail-oriented low-rank prior. The interconnection of these networks complicates the training process, necessitating a two-stage training strategy to ensure effective training. Experimental results on both simulated and real-world datasets indicate that the proposed method surpasses state-of-the-art algorithms, yielding more accurate and visually pleasing HRHS images.