Analysis Of Interest Points Of Curvelet Coefficients Contributions Of Microscopic Images And Improvement Of Edges

TL;DR

This paper analyzes Curvelet coefficients in microscopic images to enhance edge detection by combining Curvelet transform with SIFT analysis, leading to improved edge details at higher scales.

Contribution

It introduces a novel method that combines Curvelet coefficients analysis with SIFT to improve edge detection in microscopic images.

Findings

Enhanced edge details at higher decomposition scales.

Curvelet coefficients analysis improves local structure understanding.

Permutation of coefficients aids in edge enhancement.

Abstract

This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study local structure in images. The permutation of Curvelet coefficients from original image and edges image obtained from gradient operator is used to improve original edges. Experimental results show that this method brings out details on edges when the decomposition scale increases.