Modeling phase shift data of phase-detection autofocus by skew-normal distribution

Chin-Cheng Chan; Homer H. Chen

doi:10.1117/1.JEI.28.3.033001

3 May 2019 Modeling phase shift data of phase-detection autofocus by skew-normal distribution

Chin-Cheng Chan, Homer H. Chen

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033001 (May 2019). https://doi.org/10.1117/1.JEI.28.3.033001

Abstract

Recent mobile imaging seeks to expedite the autofocus process by embedding a phase detector in the image sensor to provide information for controlling both the magnitude and direction of lens movement. Compared to conventional contrast-detection autofocus, phase-detection autofocus (PDAF) is able to quickly bring the lens toward the in-focus position. However, the presence of sensor noise, the lack of image contrast, and the spatial offset between the left and right phase detectors can easily affect the performance of phase detection. We present a statistical approach to address this issue by characterizing the distribution of phase shift for a given distance of the lens to the in-focus position. We model the phase shift as a skew-normal distribution and verify it empirically. The results show that the skew-normal distribution is indeed a proper model for the phase shift data. We also propose a method based on Bayes’ theorem to determine the lens movement. Experimental results show that the proposed method is able to improve the reliability of PDAF.

Citation Download Citation

Chin-Cheng Chan and Homer H. Chen "Modeling phase shift data of phase-detection autofocus by skew-normal distribution," Journal of Electronic Imaging 28(3), 033001 (3 May 2019). https://doi.org/10.1117/1.JEI.28.3.033001

Received: 12 January 2019; Accepted: 9 April 2019; Published: 3 May 2019

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