Brightness-gradient difference feature guided shadow removal method

Abstract

Shadow removal is a fundamental and pivotal task to build the high-level cognition in the computer vision field. Due to the fact that the existing shadow removal methods cannot effectively remove shadows from the outdoor image and deal with shadow boundaries, we construct a convolutional neural network without the process of shadow detection for the shadow removal task. The constructed CNN avoids the risk of gradient vanishing by designing double-attention residual block and improves the performance of shadow removal by fusing the knowledge transfer idea. Specially, we design a hand-crafted feature, named brightness-gradient difference feature, to distinguish shadow boundary pixels from non-shadow boundary pixels, and the designed feature is fused into the loss function to dilute or even eliminate the existing shadow boundaries. Extensive experiments using three public shadow removal benchmarks with three measurable indicators are reported in this paper. The results of experiments demonstrate that the proposed method has an effective performance for the shadow removal task. The ablation studies validate the structural rationality of the proposed method.