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Open Access Autofocus by deep reinforcement learning

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In recent years, smartphones have become the primary device for day-to-day photography. Therefore, it is critical for mobile imaging to capture sharp images automatically without human intervention. In this paper, we formulate autofocus as a decisionmaking process, in which the travel distance of a lens is determined from the phase data obtained from the phase sensors of a smartphone, and the decision-making policy is based on reinforcement learning, a popular technique in the field of deep learning. We propose to use a noise-tolerant reward function to combat the noise of the phase data. In addition, instead of using only the current phase data, each lens movement is determined using the phase data acquired along the journey of an autofocus process. As a result, the proposed machine-learning approach is able to expedite the autofocus process as well. Experimental results show that the method indeed improves the autofocus speed.
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Keywords: Deep learning; Phase detection autofocus; Reinforcement learning

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "Autofocus by deep reinforcement learning".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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