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

This article 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 spatiospectral total variation as regularization and evaluates the edge similarity between HR-HS and guide images to effectively use a priori 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.

Automated summary

This article proposes a new method for estimating high spatial resolution hyperspectral images based on convex optimization. The method can simultaneously estimate a high spatial resolution image and a noiseless guide image, and effectively utilize prior knowledge and spatial detail information to improve the estimation accuracy.