Method for detecting image edge by nonsubsampled contourlet transform (NSCT)

Abstract

The invention discloses a method for detecting an image edge by nonsubsampled contourlet transform (NSCT). The method comprises the following steps of: performing NSCT on an input noise-containing image to decompose the image into a low-frequency coefficient and a high-frequency coefficient, performing multi-directional micromotion on a low-frequency coefficient matrix and each directional sub-band coefficient matrix to acquire a plurality of micromotion modulated images, subtracting each micromotion modulated image from a primary sub-band image to acquire a plurality of micromotion changed images, introducing a visual competition mechanism, taking the modulus maximum value to compete to acquire reinforced sub-band edge images, setting a proper threshold to remove noise from each sub-bandedge image, superposing a low-frequency sub-band thick edge image and each directional sub-band edge within the same scale to acquire multi-scale thick edge images, thinning the centre of the thick edge images to acquire a low-frequency sub-band thin edge image and multi-scale thin edge images, and performing OR operation to fuse the low-frequency sub-band thin edge image and the multi-scale thinedge images to acquire the finally fused edge image. The method provided by the invention has the advantages that: noise adaptability is high, and the edge is completely detected and accurately positioned.

Description

technical field The invention belongs to the technical field of image processing, and relates to an image edge detection method, in particular to an image edge detection method using non-subsampling contourlet transformation by referring to the micro-motion mechanism of human eye vision. Background technique Image edge refers to the set of pixels whose surrounding pixel gray levels have step changes and roof changes, and is one of the most basic features of images. Image edges often carry most of the information of an image. Edge detection plays an important role in computer vision, image processing and other applications, and is an important link in image analysis and recognition, so image edge detection has always been a hot topic of research. Most of the traditional edge detection methods are based on the first-order or second-order differential operators in the air domain, such as Roberts, Sobel, Laplacian and other differential operators, which are effective for clear...

AI Technical Summary

Problems solved by technology

Patent search and the latest search of various scientific and technological documents at home and abroad show that there is no non-subsampled contourlet domain edge detection method based on the human eye micro-motion mechanism that has been published in the literature

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