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Shadow Detection and Removal From Photo-Realistic Synthetic Urban Image Using Deep Learning

Hee-Jin Yoon1, Kang-Jik Kim1, Jun-Chul Chun1,*
1 Department of Computer Science, Kyonggi University, Gyeonggi-do, 16227, Korea.
* Corresponding Author: Jun-Chul Chun. Email: .

Computers, Materials & Continua 2020, 62(1), 459-472. https://doi.org/10.32604/cmc.2020.08799

Abstract

Recently, virtual reality technology that can interact with various data is used for urban design and analysis. Reality, one of the most important elements in virtual reality technology, means visual expression so that a person can experience threedimensional space like reality. To obtain this realism, real-world data are used in the various fields. For example, in order to increase the realism of 3D modeled building textures real aerial images are utilized in 3D modelling. However, the aerial image captured during the day can be shadowed by the sun and it can cause the distortion or deterioration of image. To resolve this problem, researches on detecting and removing shadows have been conducted, but the detecting and removing shadow is still considered as a challenging problem. In this paper, we propose a novel method for detecting and removing shadows using deep learning. For this work, we first a build a new dataset of photo-realistic synthetic urban data based on the virtual environment using 3D spatial information provided by VWORLD. For detecting and removing shadow from the dataset, firstly, the 1-channel shadow mask image is inferred from the 3-channel shadow image through the CNN. Then, to generate a shadow-free image, a 3-channel shadow image and a detected 1-channel shadow mask into the GAN is executed. From the experiments, we can prove that the proposed method outperforms the existing methods in detecting and removing shadow.

Keywords

Deep-learning, shadow detection, shadow removal, synthetic data.

Cite This Article

H. Yoon, K. Kim and J. Chun, "Shadow detection and removal from photo-realistic synthetic urban image using deep learning," Computers, Materials & Continua, vol. 62, no.1, pp. 459–472, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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