Shading-aware shadow detection and removal from a single image
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ORIGINAL ARTICLE
Shading-aware shadow detection and removal from a single image Xinyun Fan1 · Wenjun Wu1 · Ling Zhang2 · Qingan Yan3 · Gang Fu1 · Zipei Chen1 · Chengjiang Long4 · Chunxia Xiao1
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Shadow removal is a challenging problem due to its sensitivity to lighting and material conditions. In this paper, we propose a shading-aware shadow processing algorithm, which can automatically detect and remove complex shadows from a single color image. Our framework consists of two key steps. We firstly conduct a shadow-preserving filter upon the image which will effectively remove the image texture while preserving the shadow and shading information. Shadow regions are estimated by establishing a confidence map from the filtered image incorporating depth cue. We then develop a shading-aware optimization framework to remove shadows and recover shading in these regions. The extensive experimental results show that the proposed algorithm produces visually compelling results in a series of challenging images and it can handle complex shadows in both indoor and outdoor scenes. Quantitative and qualitative comparisons with current state-of-the-art methods strongly demonstrate the efficacy of our proposed approach. Keywords Shadow removal · Complex shadow · Image processing
1 Introduction
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Chunxia Xiao [email protected] Xinyun Fan [email protected] Wenjun Wu [email protected] Ling Zhang [email protected] Qingan Yan [email protected] Gang Fu [email protected] Zipei Chen [email protected] Chengjiang Long [email protected]
1
School of Computer, Wuhan University, Wuhan 430072, China
2
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China
3
JD.com American Technologies Corporation, Mountain View, CA 94043, USA
4
JD Digits, Mountain View, CA 94043, USA
Shadow is a ubiquitous natural phenomenon in our daily life. Although shadows can provide useful clues for illumination estimation [46], scene depiction [20] and object shapes [32], shadows also degrade the performance of some applications, such as object recognition [7], object tracking [29] and intrinsic image decomposition [25]. Therefore, it is a fundamental problem to detect and remove shadows from single images and will definitely be beneficial for computer vision and graphics communities. Shadow removal involves three main challenges. First, for the image with complex shadows like a surface with both soft and hard shadow, accurate shadow detection is challenging. Second, there are usually texture details losing on hard shadow boundaries, which will induce visual artifacts on these boundaries during shadow removing [40,48]. Finally, to obtain visually consistent shadow removal results, the shading information should be preserved in the shadow-free image [42]. To overcome the above challenges, we propose an automatic shadow detection and removal method by jointly exploring color cues as well as depth information. First, based on the obse
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