Depth-of-field achieving method based on Open graphics library (GL)

Abstract

An embodiment of the invention discloses a depth-of-field achieving method based on an Open graphics library (GL). The method includes the following steps: obtaining original color buffering information of an image, wherein the original color buffering information comprises depth buffering information and color buffering information; calculating new color buffering information according to the depth buffering information and the color buffering information; calculating Poisson distribution circle diameter of a pixel point of the image, and calculating image level information according to the Poisson distribution circle diameter of the pixel point; calculating final color buffering information according to the original color buffering information, the new color buffering information, the Poisson distribution circle diameter of the pixel point and the image level information and rendering the image. By combining the Mipmap principle based on the OpenGL and the Poisson distribution, different levels of image scene information is calculated to achieve depth of field, the depth of field is achieved, and depth-of-field achieving efficiency is improved.

Description

technical field [0001] The invention relates to the field of image / video processing, in particular to a method for realizing depth-of-field effects based on OpenGL. Background technique [0002] Depth of field refers to the object distance range measured along the axis of the imaging depth of field camera that can obtain clear images in front of the camera lens or other imagers. After the focus is completed, a clear image can be formed in the range before and after the focus. This distance range before and after is called the depth of field. There is a certain length of space in front of the lens (before and after the focusing point). When the subject is located in this space, its image on the film is just between the two circles of confusion before and after the focus. The length of the space where the subject is located is called the depth of field. In other words, for the subjects in this space, the blurring degree of the image presented on the negative plane is within ...

AI Technical Summary

Problems solved by technology

[0008] The efficiency of realizing the depth of field is low, especially in the process of blurring, the Gaussian blurring method is very inefficient;

[0009] The effect of achieving depth of field is not ideal, because the Poisson distribution sampling has uncertainty, which limits the effect of depth of field

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