Shadow Detection and Removal from video using Deep Learning

Abstract

The removal of shadow from images is crucial in computer vision as it can enhancethe interpretability and visual quality of images. This research work proposesa cascade U-Net architecture for the shadow removal, consisting of twostages of U-Net Architecture. In the first stage, a U-Net is trained using theshadow images and their corresponding ground truth to predict the shadow freeimages. The second stage uses the predicted shadow free images and groundtruth as input to another U-Net, which further refines the shadow removal results.This cascade U-Net architecture enables the model to learn and refine theshadow removal progressively, leveraging both the initial predictions and groundtruth.Experimental evaluations on benchmark datasets demonstrate that our approachachieves notably good performance in both qualitative and quantitative evaluations.By using both objective metrics such as Structural Similarity Index(SSIM),and Root mean Square Error (RMSE), and subjective evaluations where humanobservers rate the quality of the shadow removal results, our approach was foundto outperform other state-of-the-art methods. Overall, our proposed cascade UNetarchitecture offers a promising solution for the shadow removal that canimprove image quality and interpretability