Regional Attention for Shadow Removal

TL;DR

This paper introduces a lightweight regional attention framework for shadow removal that improves accuracy and efficiency by effectively capturing regional contextual information, suitable for practical use.

Contribution

The work proposes a novel regional attention mechanism and a customized RASM model, enhancing shadow removal accuracy while reducing model size and computational complexity.

Findings

Outperforms state-of-the-art models in accuracy

Achieves higher efficiency and lower complexity

Demonstrates practical applicability in real-world scenarios

Abstract

Shadow, as a natural consequence of light interacting with objects, plays a crucial role in shaping the aesthetics of an image, which however also impairs the content visibility and overall visual quality. Recent shadow removal approaches employ the mechanism of attention, due to its effectiveness, as a key component. However, they often suffer from two issues including large model size and high computational complexity for practical use. To address these shortcomings, this work devises a lightweight yet accurate shadow removal framework. First, we analyze the characteristics of the shadow removal task to seek the key information required for reconstructing shadow regions and designing a novel regional attention mechanism to effectively capture such information. Then, we customize a Regional Attention Shadow Removal Model (RASM, in short), which leverages non-shadow areas to assist in restoring shadow ones. Unlike existing attention-based models, our regional attention strategy allows each shadow region to interact more rationally with its surrounding non-shadow areas, for seeking the regional contextual correlation between shadow and non-shadow areas. Extensive experiments are conducted to demonstrate that our proposed method delivers superior performance over other state-of-the-art models in terms of accuracy and efficiency, making it appealing for practical applications.