Automatic white balance method

Abstract

The invention discloses an automatic white balance method and relates to an image adjustment method, which is suitable for image processing, wherein the time of generating second image data is before the time of first image data. The method comprises the following steps: imputing the first image data; adjusting the color of the first image data according to a color gain table; executing an object detection procedure so as to remove at least one foreground object and obtaining a target background; and carrying out color difference analysis on the target background so as to decide at least one image gain parameter. The invention can automatically adjust white balance by carrying out the color difference analysis on the background.

Description

technical field [0001] The present invention relates to a method for image adjustment, and in particular to a method for automatically adjusting white balance through background color difference analysis. Background technique [0002] Under different ambient light sources, the image will show different degrees of color shift. White balance (Auto-White-Balance) control is used to adjust the phenomenon of color shift, with the goal of restoring the white in the image (reference white) to the white in the real scene. In the conventional white balance control method, the user inputs lighting conditions, such as sunset, incandescent light bulb, etc., or directly provides the reference white area in the picture. Auto white balance is for the system to detect the external ambient light source by itself to correct the color shift. However, there are many disadvantages in the existing automatic white balance technology, for example, objects that frequently enter and leave the pictu...

AI Technical Summary

Problems solved by technology

[0010] As a result, the accuracy of existing object segmentation algorithms has not yet reached a satisfactory level, so there are many limitations in application, such as:

[0011] 1. When the object is quite close to the background color feature, the existing object cutting algorithm is not easy to cut accurately

[0012] 2. The existing object cutting algorithm is prone to the phenomenon that the object is disconnected due to inadvertent cutting (such as: a part of the body is similar to the background color), and then a single object is judged as two objects

[0013] 3. When there are light reflections and shadow changes in the picture, the existing object cutting algorithm is not easy to cut accurately, but it is easy to cut out the light and shadow changes as new foreground objects, which increases the number of false alarms

Therefore, existing object segmentation algorithms need to be improved

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