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Proceedings Paper

Shift-variant image deblurring for machine vision: one-dimensional blur
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Paper Abstract

Image deblurring is an important preprocessing step in the inspection and measurement applications of machine vision systems. A computational algorithm and analysis are presented for a new approach to one-dimensional shift-variant image deblurring. The new approach is based on a new mathematical transform that restates the traditional shift-variant image blurring model in a completely local but exactly equivalent form. The new approach is computationally noniterative, efficient, and permits very fine-grain parallel implementation. The theory of the new approach for onedimensional shift-variant deblurring is presented. Further, its advantages in comparison with related approaches, and experimental results are presented.

Paper Details

Date Published: 10 September 2009
PDF: 12 pages
Proc. SPIE 7432, Optical Inspection and Metrology for Non-Optics Industries, 743209 (10 September 2009); doi: 10.1117/12.825663
Show Author Affiliations
Muralidhara Subbarao, SUNY, Stony Brook (United States)
Youn-sik Kang, SUNY, Stony Brook (United States)
Xue Tu, SUNY, Stony Brook (United States)


Published in SPIE Proceedings Vol. 7432:
Optical Inspection and Metrology for Non-Optics Industries
Peisen S. Huang; Toru Yoshizawa; Kevin G. Harding, Editor(s)

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