New Divergence and Entropy Measures for Intuitionistic Fuzzy Sets on Edge Detection

TLDR: A new and significant technique has been developed for edge detection and mean square error (MSE) and peak signal-to-noise ratio (PSNR) have been calculated and PSNRvalues of proposed method are always equal or greater than the PSNR values of existing methods.

Abstract: Edges of the image play an important role in the field of digital image processing and computer vision. The edges reduce the amount of data, extract useful information from the image and preserve significant structural properties of an input image. Further, these edges can be used for object and facial expression detection. In this paper, we will propose new intuitionistic fuzzy divergence and entropy measures with its proof of validity for intuitionistic fuzzy sets. A new and significant technique has been developed for edge detection. To check the robustness of the proposed method, obtained results are compared with Canny, Sobel and Chaira methods. Finally, mean square error (MSE) and peak signal-to-noise ratio (PSNR) have been calculated and PSNR values of proposed method are always equal or greater than the PSNR values of existing methods. The detected edges of the various sample images are found to be true, smooth and sharpen.