Novel Approach of Blurriness Reduction from Image by Particle Swarm Optimization

Abstract

Blurring of images can be caused by movement of object or camera while capturing the image. The DE blurring of Images is the reconstruction or restoration of the uncorrupted image from a distorted and noisy one. In this paper, an idea for two directional image deblurring algorithm is introduced which uses basic concepts of PDEs. Motion Blurring is introduced in two directions: horizontal and vertical. Then we proposed PDEs based model for image deblurring considering both the directions which is based on the mathematical model. A simple two dimensional algorithm has been introduced and implemented. The results show better quality of images by applying this algorithm. In this research various methods for noise reduction have been analyzed. In the analysis, various well-known measuring metrics have been used. The results show that by using the PDE technique noise reduction is much better compared to other methods. In addition, by using this method the quality of the image is better enhanced. Using PDE the unconstrained image problem can be easily done regularized. The median, mean and wiener filters have low PSNR values for Gaussian noise. Weiner filtering is the worst case for such noises. The PDE technique is much efficient than these for the motion blurring. The vertical deblurring shows the better results than horizontal and combined deblurring in PDE.