Blur identification and image restoration based on evolutionary multiple object segmentation for digital auto-focusing
- Authors
- Shin, JH; Hwang, SY; Kim, KM; Kang, JY; Lee, SW; Paik, J
- Issue Date
- Dec-2004
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- COMBINATORIAL IMAGE ANALYSIS, PROCEEDINGS, v.3322, pp 656 - 668
- Pages
- 13
- Journal Title
- COMBINATORIAL IMAGE ANALYSIS, PROCEEDINGS
- Volume
- 3322
- Start Page
- 656
- End Page
- 668
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40232
- DOI
- 10.1007/978-3-540-30503-3_50
- ISSN
- 0302-9743
1611-3349
- Abstract
- This paper presents a digital auto-focusing algorithm based on evolutionary multiple object segmentation method. Robust object segmentation can be conducted by the evolutionary algorithm on an image that has several differently out-of-focused objects. After segmentation is completed, point spread functions (PSFs) are estimated at differently out-of-focused objects and spatially adaptive image restorations are applied according to the estimated PSFs. Experimental results show that the proposed auto-focusing algorithm can efficiently remove the space-variant out-of-focus blur from the image with multiple, blurred objects.
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- Appears in
Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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