Appearance Harmonization for Single Image Shadow Removal

TL;DR

This paper introduces an automatic shadow region harmonization method that enhances the visual consistency of shadow removal results by using shadow-guided patch-based synthesis and confidence-based refinement.

Contribution

It presents a novel fully automatic approach for shadow harmonization that improves appearance consistency in shadow removal, outperforming previous methods.

Findings

Improves visual consistency of shadow removal

Outperforms state-of-the-art on benchmark datasets

Effective in handling unique image patterns

Abstract

Shadows often create unwanted artifacts in photographs, and removing them can be very challenging. Previous shadow removal methods often produce de-shadowed regions that are visually inconsistent with the rest of the image. In this work we propose a fully automatic shadow region harmonization approach that improves the appearance compatibility of the de-shadowed region as typically produced by previous methods. It is based on a shadow-guided patch-based image synthesis approach that reconstructs the shadow region using patches sampled from non-shadowed regions. The result is then refined based on the reconstruction confidence to handle unique image patterns. Many shadow removal results and comparisons are show the effectiveness of our improvement. Quantitative evaluation on a benchmark dataset suggests that our automatic shadow harmonization approach effectively improves upon the state-of-the-art.