Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization

TL;DR

This paper introduces a convex optimization-based method for hyperspectral image fusion that simultaneously denoises guide images and estimates high-resolution hyperspectral images, leveraging spatial and spectral regularization.

Contribution

It presents a novel joint estimation framework that handles noisy guide images and improves hyperspectral image fusion using hybrid total variation and edge similarity.

Findings

Outperforms existing hyperspectral image fusion methods.

Effectively denoises guide images contaminated by heavy noise.

Demonstrates superior spatial and spectral reconstruction quality.

Abstract

The paper proposes a new high spatial resolution hyperspectral (HR-HS) image estimation method based on convex optimization. The method assumes a low spatial resolution HS (LR-HS) image and a guide image as observations, where both observations are contaminated by noise. Our method simultaneously estimates an HR-HS image and a noiseless guide image, so the method can utilize spatial information in a guide image even if it is contaminated by heavy noise. The proposed estimation problem adopts hybrid spatio-spectral total variation as regularization and evaluates the edge similarity between HR-HS and guide images to effectively use apriori knowledge on an HR-HS image and spatial detail information in a guide image. To efficiently solve the problem, we apply a primal-dual splitting method. Experiments demonstrate the performance of our method and the advantage over several existing methods.