Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries

Anastasia Ingacheva; Marina Chukalina; Timur Khanipov; Dmitry Nikolaev

doi:10.1117/12.2310064

13 April 2018 Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries

Anastasia Ingacheva, Marina Chukalina, Timur Khanipov, Dmitry Nikolaev

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 1069626 (2018) https://doi.org/10.1117/12.2310064
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

Motion blur caused by camera vibration is a common source of degradation in photographs. In this paper we study the problem of finding the point spread function (PSF) of a blurred image using the tomography technique. The PSF reconstruction result strongly depends on the particular tomography technique used. We present a tomography algorithm with regularization adapted specifically for this task. We use the algebraic reconstruction technique (ART algorithm) as the starting algorithm and introduce regularization. We use the conjugate gradient method for numerical implementation of the proposed approach. The algorithm is tested using a dataset which contains 9 kernels extracted from real photographs by the Adobe corporation where the point spread function is known. We also investigate influence of noise on the quality of image reconstruction and investigate how the number of projections influence the magnitude change of the reconstruction error.

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Anastasia Ingacheva, Marina Chukalina, Timur Khanipov, and Dmitry Nikolaev "Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069626 (13 April 2018); https://doi.org/10.1117/12.2310064

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KEYWORDS

Reconstruction algorithms

Tomography

Point spread functions

Expectation maximization algorithms

Photography

Cameras

Convolution

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