Author: Xing, Wei
Title: Human-themed image background defocusing on mobile devices with deep learning approaches
Advisors: Zhang, Lei (COMP)
Degree: M.Sc.
Year: 2019
Subject: Hong Kong Polytechnic University -- Dissertations
Computer vision
Image processing -- Digital techniques
Department: Department of Computing
Pages: vi, 47 pages : color illustrations
Language: English
Abstract: The system, for defocusing the background of human-themed images, is one that emphasizes people as foreground and blurring other objects as background. As mobile devices become more and more pervasive to daily life of humankinds, the background defocusing on human-themed images turn to be essential in many mobile platforms (smartphone, UAV, etc.). Within this thesis, the author designed and implemented a prototype system for defocusing the background of human-themed images on mobile devices. The aim of the system is providing a solution to generate "Bokeh" effects, which are commonly seen on SLR (Single Lens Reflex) cameras, from human-themed pictures on mobile devices (e.g. smartphones). The system discussed in this thesis mainly includes three parts for the three subtasks of image defocusing: portrait segmentation, depth estimation, image fusion and defocusing. For the first parts, the author applied a decoder-encoder structure based on DeepLab V3+ with a light-weight encoding backbone MobileNet V2. For the second part, the author applied a depth map generating method based on DenseNet. For the last part of the system, the author applied Gaussian blurring in different magnitude with the information of relative depth. As the experiments conducted on PC, the results showed that this prototype could provide state-of-art performance. The author also transplanted the prototype system to Android devices (specifically, an Android smartphone) and the experiment result showed that it cost less than one second per image on mobile devices, which means that the prototype is appropriate for being deployed on mobile devices.
Rights: All rights reserved
Access: restricted access

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