Blur Length Estimation in Linear Motion Blurred Images using Evolutionary Algorithms

Motion blur is a common blur type that crated due to motion of camera or some objects in scene‎. ‎The blur kernel‎, ‎i.e‎. ‎the function that simulate the motion blur process‎, ‎depends on the length of blur‎. ‎Therefore‎, ‎the estimation of blur length is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image‎, ‎i.e‎. ‎image deblurring‎. ‎In this paper‎, ‎a method is proposed for estimation of the motion blur length using the evolutionary methods‎. ‎To do this‎, ‎we take advantage of the relation between a blur metric‎, ‎called NIDCT‎, ‎and the blur length‎. ‎Then this relation is learned via the evolutionary algorithms‎. ‎The learned relation can be used to estimate the motion blur length in a blurred image‎. ‎The efficiency of the proposed method is demonstrated by performing several quantitative and qualitative experiments‎.