Title: A hybrid of local and global atmospheric scattering model for depth prediction via cross Bayesian model

Authors: Qianjin Zhao; Haitao Zhang; Jianhua Cui; Yanguang Sun; Songsong Duan; Chenxing Xia; Xiuju Gao

Addresses: College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, China ' College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, China ' Anyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., Anyang, Henan, China ' College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, China; Anhui Purvar Bigdata Technology Co., Ltd., Huainan, Anhui, China ' College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, China; Anhui Purvar Bigdata Technology Co., Ltd., Huainan, Anhui, China ' College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, China; Anhui Purvar Bigdata Technology Co., Ltd., Huainan, Anhui, China ' College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui, China

Abstract: Monocular depth estimation is a fascinating and challenging problem in virtual vision. However, the training of networks based on deep learning largely depends on the training data. This paper proposes a depth prediction method based on the depth cue: atmospheric light scattering, which can effectively predict the depth in different atmospheric light scenarios. But the assumption of global atmospheric light constancy can produce the unavoidable error. Especially for complex scenes, the complex reflected light of the scene leads to uneven distribution of atmospheric light. This paper proposes a new local atmospheric light estimation method, which can simulate the real distribution of atmospheric light scattering in the air more effectively. And the experiments found that the two models are complementary. In order to fuse the intrinsic real information of the two models, this paper adopts the fusion strategy based on the Bayesian model, and edge-preserving filtering is used to preserve the detailed information.

Keywords: depth estimation; global atmospheric light; local atmospheric light; cross Bayesian model.

DOI: 10.1504/IJCSE.2022.124555

International Journal of Computational Science and Engineering, 2022 Vol.25 No.4, pp.448 - 459

Received: 04 May 2021
Accepted: 10 Aug 2021
Published online: 28 Jul 2022 *

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