A HYBRID CNN–RETINEX FRAMEWORK WITH CONFIDENCE-WEIGHTED REFINEMENT FOR ROBUST SHADOW DETECTION AND REMOVAL

Abstract

Shadow removal and detection is a critical computer vision problem because shadows usually degrade image quality and affect downstream applications such as object detection and scene understanding. Existing methods like the benchmarking framework in Vicente et al. (2023) provide useful datasets and baseline techniques but are still plagued with accurate shadow boundary refinement and realistic shadow removal reconstruction. In this paper, we present a confidence-weighted blending hybrid CNN–Retinex model for robust shadow detection and removal. The approach initially generates initial candidate shadow masks through HSV color space thresholding, which are further filtered using a CNN-based patch classifier for precise localization. For shadow removal, we have employed a Retinex-based illumination correction technique followed by an innovative confidence-weighted blending approach for unnoticeable blending of the shadowed and non-shadowed regions. We evaluate our method using the benchmark set suggested by Vicente et al. and other real images. Experimental results indicate significant improvement in quantitative metrics (PSNR, SSIM, Precision, Recall, F1, mAP) and qualitative visual quality compared to the baseline method. key words