Recasting Regional Lighting for Shadow Removal
Yuhao Liu Zhanghan Ke Ke Xu Fang Liu Zhenwei Wang Rynson W. H. Lau
Removing shadows requires an understanding of both lighting conditions and
object textures in a scene. Existing methods typically learn pixel-level color
mappings between shadow and non-shadow images, in which the joint modeling of
lighting and object textures is implicit and inadequate. We observe that in a
shadow region, the degradation degree of object textures depends on the local
illumination, while simply enhancing the local illumination cannot fully
recover the attenuated textures. Based on this observation, we propose to
condition the restoration of attenuated textures on the corrected local
lighting in the shadow region. Specifically, We first design a shadow-aware
decomposition network to estimate the illumination and reflectance layers of
shadow regions explicitly. We then propose a novel bilateral correction network
to recast the lighting of shadow regions in the illumination layer via a novel
local lighting correction module, and to restore the textures conditioned on
the corrected illumination layer via a novel illumination-guided texture
restoration module. We further annotate pixel-wise shadow masks for the public
SRD dataset, which originally contains only image pairs. Experiments on three
benchmarks show that our method outperforms existing state-of-the-art shadow
removal methods.