A Defocus Blurred Image Sharpness Detection Method Based on Edge Intensity Weight

Abstract

The invention provides a defocus blurred image definition detecting method based on edge strength weight. The defocus blurred image definition detecting method comprises the following steps: firstly, pre-processing an input image, correcting brightness and a contrast ratio through a histogram equalization method, performing Wiener filtering processing on constant power addition noises in a digital camera system, and respectively processing impulse noises and Gaussian noises through a median filter and a Gaussian filter; then, adopting four direction edge gradient operators to detect gradient of each pixel point, eliminating interferences of local bright dark points and isolated noise points according to the detected gradient size, and further processing the residual pixels; comparing direction gradient of the residual pixels with a set threshold value, distinguishing strong edge pixels with relatively large edge gradient values and weak edge pixels with relatively small boundary vicinity gradient values, and respectively endowing different weights; finally, adding up maximum gradient square of all pixels to obtain a definition detected value of the whole image.

Description

technical field [0001] The invention relates to a method for detecting the sharpness of a blurred image, in particular to a method for detecting the sharpness of a defocused blurred image based on an edge intensity weight, and belongs to the fields of digital image processing and photoelectric tracking measurement. This method realizes automatic focus detection based on digital image processing technology, evaluates the clarity of each frame of digital image collected by the lens and photoelectric sensor and judges the most accurately focused image, and effectively provides feedback and guidance for subsequent control of lens movement , on the basis of maintaining the low complexity of the existing algorithm, the function of detecting the gradient direction and distinguishing strong and weak edges is added, which objectively reflects the clarity of the image, and effectively improves the sensitivity, noise resistance and brightness change resistance of the algorithm. It can be...

AI Technical Summary

Problems solved by technology

Among the above methods, the operator of the time domain method is simple, the calculation speed is fast, but the accuracy is not high, the sensitivity of the frequency domain method is improved, but the complexity is high, and the practicality is limited. Background noise, lighting conditions, etc. are sensitive, and this type of evaluation function may fail in complex scenes

Therefore, at present, various automatic focus evaluation algorithms based on digital image processing have their own limitations, and there are various shortcomings, which need to be further improved.

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