Automatic Correction of Barrel Distorted Images Using a Cascaded Evolutionary Estimator

Abstract

All optical systems are to some extent burdened by one or more aberrations. Barrel distortion of an image is also an aberration. In this paper we used an innovative method to solve the problem of the centric radial distortion of a static image which serves for biometric identification of persons using 2D contour of a human hand. The method proposed uses a cascaded arrangement of two algorithms – the classic meta-heuristic, referred to as “jDE-differential evolution” and an algorithm called Covariance Matrix Adaptation Evolution Strategy. Optimizers use methods of inverse engineering and numerical mathematics to resolve the question of how to determine the correct parameters of the algebraic polynomial equation of the nth degree, by the application of which it is possible to obtain an image free of barrel distortion from an image affected by this distortion. The proposed method provides a high-quality and time-acceptable method of optimization and the option of choosing the approximation accuracy. With the use of the coefficients obtained, it is then possible to use a method called back-mapping to permanently correct the centric radial distortion aberration in the biometric scanner. Extensive experiments presented in this paper enable a better understanding of relationships, the accuracy obtained, and options of using evolutionary optimizers in a larger sense.