Comparative Analysis and Implementation of Image Debluring system using Particle Swarm Optimization

Abstract

Image deblurring is an old issue in image processing, yet it keeps on pulling in the consideration of specialists
and experts alike. The blurring, or degradation, of an image can be caused by many factors such as movement during the
image capture process, by the camera or, when long exposure times are used, by the subject, out-of-focus optics, use of a
wide-angle lens, atmospheric turbulence, or a short exposure time, which reduces the number of photons captured or
scattered light distortion in confocal microscopy In this paper work, an efficient image deblurring model is proposed based
on Particle Swarm Optimization (PSO) algorithm. This paper deals with the motion blurred images and how to improve the
quality of those images by estimating PSF values using trajectory curve that can help to deblur an image. Particle swarm
optimization (PSO) is a computational strategy that optimizes a problem by iteratively attempting to improve a candidate
solution with respect to a given measure of value. PSO optimizes a problem by having a number of candidate solutions, here
named particles, and moving these particles around in the search space according to simple mathematical formulae and
moving these particle as indicated by particle's position and velocity. Experimental results illustrate performance
proposed approach.