  The key to shadow removal is recovering the contents of the shadow regions
with the guidance of the non-shadow regions. Due to the inadequate long-range
modeling, the CNN-based approaches cannot thoroughly investigate the
information from the non-shadow regions. To solve this problem, we propose a
novel cleanness-navigated-shadow network (CNSNet), with a shadow-oriented
adaptive normalization (SOAN) module and a shadow-aware aggregation with
transformer (SAAT) module based on the shadow mask. Under the guidance of the
shadow mask, the SOAN module formulates the statistics from the non-shadow
region and adaptively applies them to the shadow region for region-wise
restoration. The SAAT module utilizes the shadow mask to precisely guide the
restoration of each shadowed pixel by considering the highly relevant pixels
from the shadow-free regions for global pixel-wise restoration. Extensive
experiments on three benchmark datasets (ISTD, ISTD+, and SRD) show that our
method achieves superior de-shadowing performance.
