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  • Received: Sep. 14, 2018

    Accepted: Mar. 8, 2019

    Posted: Jun. 5, 2019

    Published Online: Jun. 5, 2019

    The Author Email: Wenbin Wang (wenbin_wang@126.com)

    DOI: 10.3788/COL201917.061001

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    Long Li, Zhiyan Pan, Haoyang Cui, Jiaorong Liu, Shenchen Yang, Lilan Liu, Yingzhong Tian, Wenbin Wang. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus[J]. Chinese Optics Letters, 2019, 17(6): 061001

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Chinese Optics Letters, Vol. 17, Issue 6, 061001 (2019)

Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus

Long Li1,2, Zhiyan Pan1, Haoyang Cui1, Jiaorong Liu1, Shenchen Yang1, Lilan Liu1,2, Yingzhong Tian1,2, and Wenbin Wang3,*

Author Affiliations

  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
  • 2Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China
  • 3Mechanical and Electrical Engineering School, Shenzhen Polytechnic, Shenzhen 518055, China

Abstract

Depth from focus (DFF) is a technique for estimating the depth and three-dimensional (3D) shape of an object from a multi-focus image sequence. At present, focus evaluation algorithms based on DFF technology will always cause inaccuracies in deep map recovery from image focus. There are two main reasons behind this issue. The first is that the window size of the focus evaluation operator has been fixed. Therefore, for some pixels, enough neighbor information cannot be covered in a fixed window and is easily disturbed by noise, which results in distortion of the model. For other pixels, the fixed window is too large, which increases the computational burden. The second is the level of difficulty to get the full focus pixels, even though the focus evaluation calculation in the actual calculation process has been completed. In order to overcome these problems, an adaptive window iteration algorithm is proposed to enhance image focus for accurate depth estimation. This algorithm will automatically adjust the window size based on gray differences in a window that aims to solve the fixed window problem. Besides that, it will also iterate evaluation values to enhance the focus evaluation of each pixel. Comparative analysis of the evaluation indicators and model quality has shown the effectiveness of the proposed adaptive window iteration algorithm.

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