On Shadow Removal from Images

Abstract

This study proposes a comprehensive methodology for enhancing image quality through refined shadow removal using deep learning techniques and advanced post-processing methods. The approach is built upon a carefully designed architecture comprising a Generator and a Discriminator, working within an adversarial framework to produce high-fidelity shadow-free images. Additionally, the integration of the UNet model en hances the system’s ability to remove shadows effectively by preserving spatial infor mation and capturing both local and global features. Furthermore, the incorporation of a Multi-Head Attention mechanism within the Generator module facilitates the precise capture of long-range dependencies and enhances contextual understanding for improved shadow removal performance. The training strategy employs adversar ial learning with a Generative Adversarial Network (GAN) framework and leverages the L1 loss function to optimize the model parameters iteratively. Additionally, post processing techniques are introduced to refine the shadow-free images, ensuring the preservation of shadow boundaries and enhancing overall image aesthetics. Qualita tive and quantitative assessments demonstrate the efficacy of the proposed method ology in outperforming state-of-the-art approaches in shadow removal performance, highlighting its potential to significantly improve image processing tasks related to shadow removal across various applications..