Dynamic video image clarity enhancement method and device

Abstract

The invention relates to the technical field of image processing, and discloses a method and device for enhancing the definition of dynamic video images. The method includes the steps of: obtaining the YUV data of the current pixel, normalizing the YUV data of the current pixel; calculating the neighborhood blur data and image texture data of the normalized Y component; using all the YUV data of the Y component. The neighborhood blur data and the image texture data are sharpened and enhanced, and the Y component after sharpening and enhancement is adjusted to the overall contrast; the adjusted Y component and the UV component of the current pixel are used to calculate the RGB data of the current pixel, and the The RGB data of the current pixel. The technical solution of the present invention achieves efficient sharpness enhancement by quickly improving the color difference of different things in the image, and can meet the continuous processing requirements of dynamic video images.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method and device for enhancing the definition of a dynamic video image. Background technique [0002] Photography and camera equipment can help people record images so that they can be viewed at any time and place, but limited by the equipment and the ability of the photographer, the picture quality presented by many image materials is not ideal, and it is difficult to meet the needs of users. In the case of high cost of time and manpower and material resources for reshooting, certain technical means are usually chosen to enhance the clarity of the image. [0003] Traditional sharpness enhancement algorithms are often aimed at specific industries or working environments, such as pattern recognition in computers, medical X-ray imaging, meteorological imaging, etc. These only need to process individual static images, and the real-time requirements for processing are not...

AI Technical Summary

Problems solved by technology

[0003] Traditional sharpness enhancement algorithms are often aimed at specific industries or working environments, such as pattern recognition in computers, medical X-ray imaging, meteorological imaging, etc. These only need to process individual static images, and the real-time requirements for processing are not high. The processing capacity is usually large, which cannot meet the efficiency and performance requirements of continuous processing of dynamic images

In addition, the image enhancement methods in the prior art often only perform one-sided enhancement for a specific requirement, such as enhancing brightness, enhancing contrast, enhancing chroma, etc. Although the enhancement range is relatively large, the algorithm is generally relatively single. To enhance multiple parameters of the image, multiple algorithms need to be run separately, which is too large in calculation and poor in real-time performance

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