Definition enhancement method and apparatus for dynamic video image

Abstract

The invention, which relates to the technical field of image processing, discloses a definition enhancement method and apparatus for a dynamic video image. The method comprises: YUV data of a current pixel are obtained and normalization processing is carried out on the YUV data of the current pixel; neighborhood fuzzy data and image texture data of a Y component after normalization are calculated; sharpening enhancement is carried out by using the neighborhood fuzzy data and image texture data of the Y component and an overall contrast ratio of the Y component after sharpening enhancement is adjusted; and RGB data of the current pixel are calculated by using the adjusted Y component and a UV component of the current pixel and the RGB data of the current pixel are outputted. According to the technical scheme, high-efficiency definition enhancement is realized by improving aberration of different objects in the image rapidly; and the continuous processing demand of the dynamic video image can be satisfied.

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

Images