Abstract
Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.
Highlights
In imaging science, image processing plays a vital role in the analysis and interpretation of images in fields such as medical discipline, navigation, environment modeling, automatic event detection, surveillance, texture and pattern recognition, and damage detection
During the image processing operation, a photograph or a video frame is analyzed with a chosen signal processing technique and the outcomes such as processed image, data, and/or parameters related to image are further investigated to extract the desired information from the raw input image
In bilevel thresholding, input image is divided into two classes such as C0 and C1 by a threshold at a level “t.” The class C0 encloses the gray levels in the range 0 to t − 1 and class C1 encloses the gray levels from t to L − 1
Summary
Image processing plays a vital role in the analysis and interpretation of images in fields such as medical discipline, navigation, environment modeling, automatic event detection, surveillance, texture and pattern recognition, and damage detection. The development of digital imaging techniques and computing technology increased the potential of imaging science. Image segmentation is one of preprocessing techniques used to regulate the features of an image. It is judged to be an important procedure for significant examination and interpretation of input images [1]. Several techniques for segmentation have been proposed and implemented in the literature [2,3,4,5,6,7,8,9,10]. The input image is separated into nonoverlapping, homogenous regions containing similar objects. Based on the performance appraisal process, the segmentation methods are classified into two groups such as supervised and unsupervised evaluation. Unsupervised methods are preferable in real-time processing because they do not require a manually segmented image [11]
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