Efficient Model-Driven Network for Shadow Removal


  • Yurui Zhu University of Science and Technology of China
  • Zeyu Xiao University of Science and Technology of China
  • Yanchi Fang University of Toronto
  • Xueyang Fu University of Science and Technology of China
  • Zhiwei Xiong University of Science and Technology of China
  • Zheng-Jun Zha University of Science and Technology of China




Computer Vision (CV)


Deep Convolutional Neural Networks (CNNs) based methods have achieved significant breakthroughs in the task of single image shadow removal. However, the performance of these methods remains limited for several reasons. First, the existing shadow illumination model ignores the spatially variant property of the shadow images, hindering their further performance. Second, most deep CNNs based methods directly estimate the shadow free results from the input shadow images like a black box, thus losing the desired interpretability. To address these issues, we first propose a new shadow illumination model for the shadow removal task. This new shadow illumination model ensures the identity mapping among unshaded regions, and adaptively performs fine grained spatial mapping between shadow regions and their references. Then, based on the shadow illumination model, we reformulate the shadow removal task as a variational optimization problem. To effectively solve the variational problem, we design an iterative algorithm and unfold it into a deep network, naturally increasing the interpretability of the deep model. Experiments show that our method could achieve SOTA performance with less than half parameters, one-fifth of floating-point of operations (FLOPs), and over seventeen times faster than SOTA method (DHAN).




How to Cite

Zhu, Y., Xiao, Z., Fang, Y., Fu, X., Xiong, Z., & Zha, Z.-J. (2022). Efficient Model-Driven Network for Shadow Removal. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3), 3635-3643. https://doi.org/10.1609/aaai.v36i3.20276



AAAI Technical Track on Computer Vision III