An image definition evaluation method based on strong edge detection

Abstract

An image definition evaluation function method based on strong edge detection comprises the following steps of step 1, setting an edge detection operator, namely an eight-direction Sobel operator; andstep 2, setting of a judgment image definition evaluation function, wherein the intensity mean value of the strong edge pixels is adopted, and the adopted eight-direction template is the converted template shown in the step 1. The method has the advantages that the eight-direction template is adopted, gray differences in different directions are considered, and the edge extraction effect is enhanced; by considering the spatial position relation between the positive direction of the current point and surrounding points, weights which cannot be too large in difference should be given, and introduction of noise is avoided; the calculation formula of the edge intensity, namely the gradient value of each pixel point in the image, adopts a normal form 1 calculation method, so that the time required by square resquaring operation is greatly saved, and the extraction of the edge points of the image is facilitated.

Description

technical field [0001] The invention relates to a method for evaluating image definition. technical background [0002] With the rapid development of various digital imaging technologies, obtaining high-quality clear images is of great significance for the imaging system itself and subsequent image processing (pattern recognition). In the image acquisition system, in order to obtain a clear image, the adjustment of the focal length is particularly important, which requires the camera system to be able to automatically adjust the focus on the basis of obtaining image clarity. Most auto-focus algorithms are based on image processing, that is, by judging the image clarity, the current focusing state of the imaging system is obtained, and then the focusing mechanism is driven according to the focusing control strategy to obtain the clearest target image. How to objectively and effectively evaluate the sharpness of different images is still a research hotspot. Therefore, image ...

AI Technical Summary

Problems solved by technology

[0004] These methods have their own advantages and disadvantages. For example, the spatial domain function evaluation is fast and concise, but it is not sensitive to small changes in image details; although the frequency domain function is sensitive to sharpness changes, the calculation amount is large and slow, which cannot meet the real-time effectiveness requirements. ;Based on the entropy function of informatics, the information entropy when the image is in focus is larger than the information entropy when it is out of focus to distinguish different resolutions, but the content of the image will fluctuate greatly, or even have the opposite result, so it cannot be evaluated Different images; based on statistical functions, the measurement accuracy is not very high, and the evaluation results are not accurate

Images