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Shadow removal is an essential task for scene understanding. Many studiesconsider only matching the image contents, which often causes two types ofghosts: color in-consistencies in shadow regions or artifacts on shadowboundaries. In this paper, we tackle these issues in two ways. First, tocarefully learn the border artifacts-free image, we propose a novel networkstructure named the dual hierarchically aggregation network~(DHAN). It containsa series of growth dilated convolutions as the backbone without anydown-samplings, and we hierarchically aggregate multi-context features forattention and prediction, respectively. Second, we argue that training on alimited dataset restricts the textural understanding of the network, whichleads to the shadow region color in-consistencies. Currently, the largestdataset contains 2k+ shadow/shadow-free image pairs. However, it has only 0.1k+unique scenes since many samples share exactly the same background withdifferent shadow positions. Thus, we design a shadow matting generativeadversarial network~(SMGAN) to synthesize realistic shadow mattings from agiven shadow mask and shadow-free image. With the help of novel masks orscenes, we enhance the current datasets using synthesized shadow images.Experiments show that our DHAN can erase the shadows and produce high-qualityghost-free images. After training on the synthesized and real datasets, ournetwork outperforms other state-of-the-art methods by a large margin. The codeis available: http://github.com/vinthony/ghost-free-shadow-removal/

Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN