image processing method

Abstract

The invention provides an image processing method. The image processing method of the present invention uses two frames of multi-frame images of the scene as the reference image and the original image to be corrected respectively, and uses the peak coordinates of the cross entropy of the reference image and the original image to be corrected to obtain the restoration displacement amount. According to the restoration The original image to be corrected is shifted by the displacement amount, and the portion of the shifted image to be corrected that overlaps with the original image to be corrected and the other portions of the reference image except the portion corresponding to the overlapping portion are used to form a preliminary corrected image, and then the polygon of the reference image is calculated. The cross entropy of each replacement block and the block to be replaced corresponding to the preliminary corrected image is obtained, and the peak values ​​of multiple cross entropies are obtained. The peak value of the cross entropy that is less than the threshold is found and the corresponding replacement block is used to replace the corresponding to-be-replaced block in the preliminary corrected image. Replace blocks to effectively remove ghosts from high dynamic range images and improve image quality.

Description

technical field [0001] The present invention relates to the field of display technology, in particular to an image processing method. Background technique [0002] Sensors available in digital imaging devices such as cameras typically capture a much smaller range of brightness than the range of brightness that the human eye can see in a real scene. Traditional digital imaging equipment shoots an image of the scene with a single exposure, so the image only contains a limited range of brightness contrast. Specifically, in a longer exposure time, the exposure is higher. At this time, although the captured scene The low-brightness area can obtain relatively clear image details, but the high-brightness area will show overexposure. On the contrary, in the short exposure time, the exposure is low. Clearer image details, but low-brightness areas will show overexposure, depending on whether the exposure is high or low, and many details in the overly bright or dark areas of the scene...

AI Technical Summary

Problems solved by technology

However, real scenes often contain dynamic elements. If multiple frames of images are directly fused to form an HDR image, ghost images will be formed in the image, and the quality of the image will be affected.

In order to solve this problem, an existing scheme based on background modeling multi-exposure image fusion to remove ghosts, specifically uses median filtering to obtain background images, and subtracts them to obtain moving objects, but in fact median filtering is difficult to accurately separate the background Figures and moving objects, the processing effect is poor, and another existing scheme removes ghosting through moving object detection and extension, specifically based on the method of Markov random field framework to detect moving areas, and uses a method based on image segmentation The method to expand the motion area, the calculation of this method is complicated, and it is difficult to realize

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