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Deblurring filter design based on fuzzy regression modeling and perceptual image quality assessment

Citation

Chan, KY and Rajakaruna, N and Engelke, U, Deblurring filter design based on fuzzy regression modeling and perceptual image quality assessment, IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 9-12 November 2015, Hong Kong, pp. 2027-2032. ISBN 9781479986965 (2016) [Refereed Conference Paper]

Copyright Statement

Copyright 2015 IEEE

DOI: doi:10.1109/SMC.2015.354

Abstract

Images captured by digital cameras are generally not perfect as image blurring is usually generated by camera motion through long hand-held exposure. Deblurring filters can be used to improve image quality by removing image blur. Prior to develop a deblurring filter, a simulator for image quality assessment is essential to optimize filter parameters. Although subjective image quality assessment (subjective IQA) is commonly used for evaluating the visual effect of digital images for a wide range of image processing applications, it is inconvenient to be implemented in real-Time. Generally, statistical regression is used to generate a functional map to correlate the subjective IQA and the objective image quality metrics. However, it cannot address the uncertainty caused by human judgment during the subjective IQA. This paper first proposes a fuzzy regression method to develop the functional map that overcomes the limitation of statistical regression that cannot account for uncertainty introduced through human judgment. Based on the fuzzy regression models, the deblurring filter parameters can be optimized. Experimental results show that the satisfactory deblurring can be achieved on blurred images captured by a smartphone camera.

Item Details

Item Type:Refereed Conference Paper
Keywords:filter design, fuzzy regression, image deblurring, image quality evaluation, objective image quality metric, bandpass filters, cybernetics, fuzzy filters, image enhancement, image processing, regression analysis, filter design, fuzzy regressions
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Stochastic Analysis and Modelling
Objective Division:Information and Communication Services
Objective Group:Media Services
Objective Field:Animation and Computer Generated Imagery Services
Author:Engelke, U (Dr Ulrich Engelke)
ID Code:120043
Year Published:2016
Deposited By:Engineering
Deposited On:2017-08-09
Last Modified:2017-10-05
Downloads:0

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