Method and device for detecting image definition

Abstract

The invention provides a method for detecting image definition and a device for detecting the image definition. The method for detecting the image definition includes the steps: obtaining an input primary image; enabling the primary image to be converted into a gray level image; conducting denoising to the gray level image to obtain a denoising image; obtaining a peripheral point of the denoising image; conducting lowpass filtering operation to the denoising image to obtain a blurred image; respectively calculating image definition characteristic quantity of each peripheral point of the denoising image and image definition characteristic quantity of each peripheral point of the blurred image; calculating the image definition characteristic quantity of the each peripheral point according to the image definition characteristic quantity of the denoising image and the image definition characteristic quantity of the blurred image; and calculating and enabling an average value of the image definition characteristic quantity of all peripheral points to be used as image definition characteristic quantity of the primary quantity so as to judge the image definition characteristic quantity of the primary quantity. The method for detecting the image definition and the device for detecting the image definition can determine image definition precisely, and a suggested image definition estimation scheme can be suitable for the vast majority of images.

Description

technical field [0001] The invention relates to the technical field of image quality evaluation, in particular to a method and device for detecting image clarity. Background technique [0002] Image quality is an important indicator for judging the performance of image acquisition equipment and whether its working status is normal, and it is also widely used in image processing algorithm performance comparison and system parameter optimization. Therefore, it is of great significance to establish an effective image quality evaluation mechanism in the fields of image acquisition, encoding and compression, and network transmission. [0003] In recent years, with the development of image processing technology, research in this field has attracted extensive attention of researchers. Image quality evaluation algorithms emerge in endlessly, and image quality evaluation can be divided into two categories: subjective evaluation methods and objective evaluation methods. [0004] The...

AI Technical Summary

Problems solved by technology

At present, there are some sharpness measurement methods. Most of the algorithms extract some feature quantities from the image that can reflect the image sharpness to a certain extent, and use these feature quantities as the image sharpness measurement value. These feature quantities have certain effect, but generally has the defect that the scope of application is relatively narrow, and it can only have certain effects on certain specific images. In addition, due to the large differences in scenes, the feature quantities obtained in different scenes are not necessarily comparable.

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