Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy

Abstract

In the current era, image evaluations play a foremost role in a variety of domain, where the processing of the digital images is essential to identify the vital information. The image multi-thresholding is a vital image preprocessing field in which the available digital image is enhanced by grouping the similar pixel values. Normally, the digital test images are available in RGB/grey scale format and the appropriate processing methodology is essential to treat the images with a chosen methodology. In the proposed approach, Tsallis Entropy (TE) supported multi-level thresholding is planned for the benchmark grey scale imagery of dimension 512x512x1 pixels using a chosen threshold values (T=2,3,4,5). This work suggests the possible Cost Value (CV) that can be considered during the optimization search and the proposed work is executed by considering the maximization of the TE as the CV. The entire thresholding task is executed using Moth-Flame Algorithm (MFA) and the accomplished results are validated based on the image quality measures of various thresholds. The attained result with MFO is better compared to the result of CS, BFO, PSO and GA.