Restoration of Pansharpened Images by Conditional Filtering in the PCA Domain

TL;DR

This paper proposes a restoration method for pansharpened images that uses PCA-based conditional filtering and local histogram matching to reduce artifacts and improve spatial quality.

Contribution

It introduces a novel restoration strategy involving PCA transform and conditional filtering to enhance pansharpened images, addressing spectral and spatial distortions.

Findings

Effective artifact reduction demonstrated in experiments

Improved spatial resolution through histogram matching

Restoration chain outperforms existing methods

Abstract

Pansharpening techniques aim at fusing low-resolution multispectral (MS) images and high-resolution panchromatic (PAN) images to produce high-resolution MS images. Despite significant progress in the field, spectral and spatial distortions might still compromise the quality of the results. We introduce a restoration strategy to mitigate artifacts of fused products. After applying the Principal Component Analysis (PCA) transform to a pansharpened image, the chromatic components are filtered conditionally to the geometry of PAN. The structural component is then replaced by the locally histogram-matched PAN for spatial enhancement. Experimental results illustrate the efficiency of the proposed restoration chain.