Low-light-level image significant contour extraction method of WKPCA homogeneity degree correction nCRF inhibition

Abstract

The invention discloses a low-light-level image significant contour extraction method of WKPCA homogeneity degree correction nCRF inhibition. The method firstly proposes a WKPCA algorithm, performs feature vector angle matching (FAM) on every feature in the high-dimensional feature space, weakens or eliminates sick or abnormal feature data interference in a CRF region, and extracts major CRF components more precisely; and on such a basis, the method defines a homogeneity degree concept and a calculation method, calculates the environment-center homogeneity degree through the projection of the nCRF feature vectors on the center major components; and finally, based on the homogeneity degree, the method corrects every inhibition amount of the nCRF, enables to mutual inhibit amounts in the homogeneous region are large and the inhibition amounts in the heterogeneous region is small or free of mutual inhibition, and at the same time weakens the contour element self-inhibition effect as much as possible, thereby improving the accurary of inhibition effect. Therefore, the proposed model can more fully detect environment-center differences, reduce noise interference, inhibit texture details more precisely, and improve contour response intensity and completeness.

Description

technical field [0001] The invention belongs to a method for extracting salient contours of low-light images in complex scenes based on visual modeling, in particular to a method for extracting salient contours of low-light images suppressed by WKPCA homogeneity correction nCRF. Background technique [0002] Contour extraction plays an important role in understanding and analyzing night vision images. At present, most of the applications of night vision target detection and recognition are aimed at natural scenes, so low-light images contain a large number of natural textures (such as trees and grass), and the results of traditional edge detection operators retain a large number of non-contour edges. Composition (canny operator). Moreover, the low-light image itself has strong noise interference. How to remove these local uninteresting edges generated by texture and noise and maintain the integrity of the contour is the main problem for night vision image contour detection....

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for extracting salient contours of low-light images suppressed by WKPCA homogeneity correction nCRF, which can solve the deficiency of single azimuth weighted suppression, combined with the mechanism of biological vision, aiming at multi-dimensional feature differences of low-light images, from complex scenes Effectively suppress noise and texture in low-light images and extract salient contours

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