Artificial Bee Colony Algorithm for Image Restoration

This paper proposes a new method for image restoration using artificial bee colony algorithm (ABC algorithm). ABC algorithm combined with wiener filter is used to restore image. In the fuzzy identification, ABC algorithm is used for optimization the point spread function (PSF). In the restoring part, wiener filter is used for image restoration. Proposed method overcomes the problem of the traditional image restoring, which needs to know the PSF in advance. In addition, this method improves the performance of the traditional ABC algorithm by initializing the first group of bee colony. The pretreatment can reduce the randomness and improve the performance of ABC algorithm. Experimental results showed that the proposed method is effective for image restoration. Introduction Image restoration is the basis of many fields of research such as image processing, computer vision and so on. In the process of acquiring an image, the quality of the image often decreases because of various reasons such as motion blur, noise, and camera miscues. The process of linear image degradation is usually like as following model: n f h g + ∗ = (1) Here, g represents blurred image. h represents the point spread function. f represents the original image and n represents the noise. The purpose of image restoration is to "compensate for" or "undo" defects which degrade an image. In the traditional process of image restoration, the PSF of blurred image needs to be known in advance to restore image. However, in many practical situations, the PSF is generally difficult to determine. The information of degradation must be extracted from the blurred image to restore image. This process is called blind image restoration [1][2]. Blind image restoration is a more difficult image restoration method where image recovery is performed with little or no prior knowledge of the degrading PSF. Methods for blind image restoration can be divided into two categories. One is to identify the PSF and then estimate the image by image restoring algorithm. The other is the simultaneous estimation of the PSF and the original image[3]. In this paper, ABC algorithm is used for obtaining the PSF in the fuzzy identification and wiener filter is used for image restoration in the restoring identification [4]. Fuzzy identification and restoring identification will be alternately iterated until obtaining the restored image. ABC algorithm has many advantages such as convergence speed, strong robustness because of the information sharing and interaction of the bees. It can prevent premature entry into local optimum and improve the quality of the optimal solution. Meanwhile this paper improves the performance of the traditional ABC algorithm, using pretreatment to initialize the first group of bee colony. The pretreatment can reduce the randomness and improve the performance of ABC algorithm. Artificial bee colony (ABC) algorithm Artificial bee colony algorithm was first introduced by Dervis Karaboga and Basturk in 2005 for solving numerical optimization problems [5]. ABC algorithm has achieved better results on the optimization problems [6-9]. The bee colony consists of three kinds of bees: employed bees, 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) Copyright © 2016, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 50