Restoration of Pansharpened Images by Conditional Filtering in the PCA Domain
This addresses artifacts in fused satellite imagery for remote sensing applications, but it is incremental as it builds on existing pansharpening techniques.
The paper tackles spectral and spatial distortions in pansharpened images by introducing a restoration strategy that conditionally filters chromatic components in the PCA domain and replaces the structural component with histogram-matched PAN data, resulting in improved quality as shown in experimental results.
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.