Towards Hard and Soft Shadow Removal via Dual-Branch Separation Network and Vision Transformer
This addresses the problem of shadow removal in computer vision for improved image perception and texture recognition, though it is incremental as it builds on existing deep learning approaches.
The paper tackles image shadow removal by proposing a dual-path model that separately processes hard and soft shadows, achieving a 2.905 RMSE on the ISTD dataset and outperforming state-of-the-art methods.
Image shadow removal is a crucial task in computer vision. In real-world scenes, shadows alter image color and brightness, posing challenges for perception and texture recognition. Traditional and deep learning methods often overlook the distinct needs for handling hard and soft shadows, thereby lacking detailed processing to specifically address each type of shadow in images.We propose a dual-path model that processes these shadows separately using specially designed loss functions to accomplish the hard and soft shadow removal. The model classifies shadow types and processes them through appropriate paths to produce shadow-free outputs, integrating a Vision Transformer with UNet++ for enhanced edge detail and feature fusion. Our model outperforms state-of-the-art methods and achieves 2.905 RMSE value on the ISTD dataset, which demonstrates greater effectiveness than typical single-path approaches.