New Edge Detection Technique based on the Shannon Entropy in Gray Level Images

TL;DR

This paper introduces a new edge detection method based on Shannon entropy that improves processing speed and robustness in gray level images, outperforming previous techniques in quality and efficiency.

Contribution

The paper proposes an enhanced edge detection approach utilizing Shannon entropy, reducing CPU time and improving edge detection quality compared to existing methods.

Findings

Faster edge detection with reduced CPU time.

Robust edge detection quality across various images.

Superior performance over classic methods in experiments.

Abstract

Edge detection is an important field in image processing. Edges characterize object boundaries and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. In this paper, an approach utilizing an improvement of Baljit and Amar method which uses Shannon entropy other than the evaluation of derivatives of the image in detecting edges in gray level images has been proposed. The proposed method can reduce the CPU time required for the edge detection process and the quality of the edge detector of the output images is robust. A standard test images, the real-world and synthetic images are used to compare the results of the proposed edge detector with the Baljit and Amar edge detector method. In order to validate the results, the run time of the proposed method and the pervious method are presented. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The performance evaluation of the proposed technique in terms of the measured CPU time and the quality of edge detector method are presented. Experimental results demonstrate that the proposed method achieve better result than the relevant classic method.