首页 > 论文 > Chinese Optics Letters > 17卷 > 6期(p:061001)

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

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

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.

Newport宣传-MKS新实验室计划
补充资料

DOI:10.3788/COL201917.061001

所属栏目:Image processing

基金项目:This work was supported by the National Natural Science Foundation of China (No.?91748122), the National Science Foundation for Young Scientists of China (No.?61603237), the Shanghai Pujiang Program (No.?17PJ1402900), and the Science and Technology Commission of Shanghai Municipality (Nos. 16111107802 and 16111108202).

收稿日期:2018-09-14

录用日期:2019-03-08

网络出版日期:2019-06-05

作者单位    点击查看

Long Li:School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China
Zhiyan Pan:School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
Haoyang Cui:School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
Jiaorong Liu:School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
Shenchen Yang:School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
Lilan Liu:School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China
Yingzhong Tian:School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China
Wenbin Wang:Mechanical and Electrical Engineering School, Shenzhen Polytechnic, Shenzhen 518055, China

联系人作者:Wenbin Wang(wenbin_wang@126.com)

备注:This work was supported by the National Natural Science Foundation of China (No.?91748122), the National Science Foundation for Young Scientists of China (No.?61603237), the Shanghai Pujiang Program (No.?17PJ1402900), and the Science and Technology Commission of Shanghai Municipality (Nos. 16111107802 and 16111108202).

【1】S. Allegro, C. Chanel and J. Jacot. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Proceedings of International Conference on Image Processing. (1996).

【2】M. Cho and D. Shin. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Chin. Opt. Lett. 13, (2015).

【3】E. Krotkov, K. Henriksen and R. Kories. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. IEEE Trans. Pattern Anal. Mach. Intell. 12, (1990).

【4】M. Subbarao and J. K. Tyan. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. IEEE Trans. Pattern Anal. Mach. Intell. 20, (1998).

【5】M. T. Mahmood, A. Majid and T.-S. Choi. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. J. Photograph. Sci. 62, (2014).

【6】I. Lee, M. T. Mahmood and T. S. Choi. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Opt. Laser Technol. 45, (2013).

【7】M. T. Mahmood and T.-S. Choi. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Image Vision Comput. 28, (2010).

【8】R. LeachR. Leach. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Opt. Meas. Surf. Topogr. 23, (2011).

【9】A. Thelen, S. Frey, S. Hirsch and P. Hering. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. IEEE Trans. Image Process. 18, (2009).

【10】C. T. Tan, Y. S. Chan, J. A. Chen, T. C. Liao and M. H. Chiu. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Chin. Opt. Lett. 9, (2011).

【11】T. Zeng and J. Ding. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Chin. Opt. Lett. 16, (2018).T. Zeng and J. Ding. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Chin. Opt. Lett. 16, (2018).

【12】Y. Hou, L. Li, S. Wang and Q. Zhu. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Chin. Opt. Lett. 15, (2017).Y. Hou, L. Li, S. Wang and Q. Zhu. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Chin. Opt. Lett. 15, (2017).

【13】T. Yeo, S. H. Ong, and R. Sinniah. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Image Vision Comput. 11, (1993).

【14】W. Du, G. Zhang and L. Ye. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Sensors. 16, (2016).

【15】P. Hansson and J. Fransson. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Appl. Opt. 43, (2004).

【16】M. Lombardo, S. Serrao, N. Devaney, M. Parravano and G. Lombardo. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Sensors. 13, (2012).

【17】J. M. TenenbaumJ. M. Tenenbaum. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Accommodation in computer vision. (1971).

【18】Y. Nakagawa and S. K. Nayar. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. IEEE Trans. Pattern Anal. Mach. Intell. 16, (1994).

【19】A. S. Malik and T. S. Choi. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Pattern Recogn. 41, (2008).

【20】Y. Cheng, J. Zhu, S. Hu, L. Zhao, W. Yan, W. He, Y. He, W. Jiang and J. Liu. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. IEEE Photon. Technol. Lett. 29, (2017).

【21】I. T. PaperI. T. Paper. Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus. Math. Prob. Eng. 2016, (2016).

引用该论文

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," Chinese Optics Letters 17(6), 061001 (2019)

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF