Study of Efficient Technique Based On 2D Tsallis Entropy For Image Thresholding

TL;DR

This paper introduces a novel image thresholding method based on two-dimensional Tsallis entropy, utilizing local pixel and average gray values, which outperforms traditional Shannon-based methods in quality.

Contribution

The paper proposes a new thresholding technique using 2D Tsallis entropy derived from the histogram of pixel and local average gray values, enhancing image segmentation performance.

Findings

Achieves better thresholding results than Shannon entropy-based methods.

Demonstrates effectiveness on real-world and synthetic images.

Improves image segmentation quality.

Abstract

Thresholding is an important task in image processing. It is a main tool in pattern recognition, image segmentation, edge detection and scene analysis. In this paper, we present a new thresholding technique based on two-dimensional Tsallis entropy. The two-dimensional Tsallis entropy was obtained from the twodimensional histogram which was determined by using the gray value of the pixels and the local average gray value of the pixels, the work it was applied a generalized entropy formalism that represents a recent development in statistical mechanics. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images. The performance evaluation of the proposed technique in terms of the quality of the thresholded images are presented. Experimental results demonstrate that the proposed method achieve better result than the Shannon method.