Artificial Bee Colony Algorithm for Image Restoration

Abstract

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. 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,

2555 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: E6469018520/2020©BEIESP DOI:10.35940/ijrte.E6469.018520 Journal Website: www.ijrte.org Optimized Noise Reduction in Images Applying Artificial Bee Colony Based Technique

“…Khader AT et al in their survey of literature suggested that ABC algorithm have been applied to many different areas such as engineering design applications, bioinformatics applications, image processing applications and benchmarking optimizations [5]. Jie Yang et al, in 2016 proposed a new method for image restoration using ABC algorithm by combining it with wiener filter and showed that this method is better for image restoration [6]. In 2017, Linguo Li et al applied ABC algorithm for multi level thresholding of images and the experimental results showed that this method can search out the optimal thresholds efficiently, precisely, and speedily, and the thresholds are very close to the results examined by exhaustive searches [7].…”

An efficient of estimation stages for segmentation skin lesions based optimization algorithm

“…Yigitbasi ED et al in their work in 2013, done out the control detection by ABC and the outcomes exhibited better than the approaches of Sobel and Roberts for border detection with median filter [10]. [11,12]. In 2017, Li et al used ABC algorithm for multi-level thresholding of images and the experimental outcomes presented that this technique can search out the ideal thresholds efficiently and speedily, and the thresholds are very nearby to the outcomes studied by comprehensive examinations [13].…”