Progressive Recurrent Network for Shadow Removal
Yonghui Wang Wengang Zhou Hao Feng Li Li Houqiang Li
Single-image shadow removal is a significant task that is still unresolved.
Most existing deep learning-based approaches attempt to remove the shadow
directly, which can not deal with the shadow well. To handle this issue, we
consider removing the shadow in a coarse-to-fine fashion and propose a simple
but effective Progressive Recurrent Network (PRNet). The network aims to remove
the shadow progressively, enabing us to flexibly adjust the number of
iterations to strike a balance between performance and time. Our network
comprises two parts: shadow feature extraction and progressive shadow removal.
Specifically, the first part is a shallow ResNet which constructs the
representations of the input shadow image on its original size, preventing the
loss of high-frequency details caused by the downsampling operation. The second
part has two critical components: the re-integration module and the update
module. The proposed re-integration module can fully use the outputs of the
previous iteration, providing input for the update module for further shadow
removal. In this way, the proposed PRNet makes the whole process more concise
and only uses 29% network parameters than the best published method. Extensive
experiments on the three benchmarks, ISTD, ISTD+, and SRD, demonstrate that our
method can effectively remove shadows and achieve superior performance.