Decomposition space-variant blur in image deconvolution


F. Sroubek, J. Kamenicky, and Y. M. Lu, “Decomposition space-variant blur in image deconvolution,” IEEE Signal Processing Letters, vol. 23, no. 3, pp. 346-350, 2016.
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Date Published:

Mar 2016


Standard convolution as a model of radiometric degradation is in majority of cases inaccurate as the blur varies in space and we are thus required to work with a computationally demanding space-variant model. Space-variant degradation can be approximately decomposed to a set of standard convolutions. We explain in detail the properties of the space-variant degrada- tion operator and show two possible decomposition models and two approximation approaches. Our target application is space- variant image deconvolution, on which we illustrate theoretical differences between these models. We propose a computationally efficient restoration algorithm that belongs to a category of alternating direction methods of multipliers, which consists of four update steps with closed-form solutions. Depending on the used decomposition, two variations of the algorithm exist with distinct properties. We test the effectiveness of the decomposition models under different levels of approximation on synthetic and real examples, and conclude the letter by drawing several practical observations. 

Last updated on 07/12/2016