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

Muralidhara Subbarao; Youn-sik Kang; Xue Tu

doi:10.1117/12.825663

10 September 2009 Shift-variant image deblurring for machine vision: one-dimensional blur

Muralidhara Subbarao, Youn-sik Kang, Xue Tu

Proceedings Volume 7432, Optical Inspection and Metrology for Non-Optics Industries; 743209 (2009) https://doi.org/10.1117/12.825663
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

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.

Citation Download Citation

Muralidhara Subbarao, Youn-sik Kang, and Xue Tu "Shift-variant image deblurring for machine vision: one-dimensional blur", Proc. SPIE 7432, Optical Inspection and Metrology for Non-Optics Industries, 743209 (10 September 2009); https://doi.org/10.1117/12.825663

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KEYWORDS

Image restoration

Image filtering

Machine vision

Point spread functions

Deconvolution

Cameras

Image analysis

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