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Flexible focus function consisting of convex function and image enhancement filter
Author(s) -
Kai Wang,
Yuntao Qian,
Minchao Ye,
Zhijian Luo
Publication year - 2014
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.22.018668
Subject(s) - focus (optics) , computer vision , image restoration , filter (signal processing) , artificial intelligence , computer science , noise (video) , point spread function , image (mathematics) , function (biology) , regular polygon , optics , motion blur , image processing , mathematics , physics , geometry , evolutionary biology , biology
We propose a new focus function Λ that, like many of the existing focus functions, consists of a convex function and an image enhancement filter. Λ is rather flexible because for any convex function and image enhancement filter, it is a focus function. We proved that Λ is a focus function using a model and Jensen's inequality. Furthermore, we generated random Λs and experimentally applied them to simulated and real blurred images, finding that 98% and 99% of the random Λs, respectively, have a maximum value at the best-focused image and most of them decrease as the defocus increases. We also applied random Λs to motion-blurred images, blurred images in different-sized windows, and blurred images with different types of noise. We found that Λ can be applied to motion blur and is robust to different-sized windows and different noise types.

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