Method for synthesizing ghost-reduced high-dynamic-range video based on block matching dynamic estimation

Abstract

The invention provides a method for synthesizing a ghost-reduced high-dynamic-range (HDR) video based on a block matching dynamic estimation algorithm. The method comprises the following steps: block matching: selecting a reference frame and a current frame from an input picture sequence, performing gray-scale processing, and calculating a motion vector of the current frame through an ARPS3 block matching algorithm; detection and calibration of motion areas: detecting respective motion areas of the current frame and the reference frame through the calculated motion vector, combining the motion areas of specific images, and marking on a binary bitmap; and synthesis of a ghost-reduced HDR image: improving a weight map obtained by calculation according to an Exposure Fusion algorithm by the obtained binary bitmap, and fusing images needing to be fused under the guidance of the improved weight map in order to obtain a final HDR image, namely, video frames. According to the HDR video synthesis method, ghost areas are detected through motion estimation, and ghosts are reduced through a fusion algorithm to synthesize the HDR video frames.

Description

technical field [0001] The invention relates to the technology in the field of digital image processing, and in particular relates to a high dynamic video synthesis method based on a block matching dynamic estimation algorithm to remove ghosting. Background technique [0002] In the image field, the dynamic range (Dynamic Range) refers to the ratio between the maximum gray value and the minimum gray value within the display range of the image. For natural scenes in the real world, from direct sunlight to shadows under trees, the ratio of the brightness of the brightest light to the brightness of the darkest light is about 10 4 order of magnitude, so it is called high dynamic range. However, in most of the current color images, due to the limitation of the number of storage bits of each channel, the range that can be represented is only 0-255 gray levels, so the dynamic range of the image is called Low Dynamic Range (Low Dynamic Range, hereinafter referred to as LDR). [0...

AI Technical Summary

Problems solved by technology

This type of method needs to solve the picture calibration problem caused by the moving object too fast, because each frame of the final video is composed of a set of previous pictures with different exposures. If the moving object moves faster, it is difficult for the camera to achieve The corresponding shooting speed will lead to the final calibration of different exposure pictures using complex methods, and repeated exposure changes in a short period of time are also a harsh requirement for the equipment

[0005] HDR images are usually composed of multiple still LDR images at different exposures. However, this method is not suitable for HDR videos, because when shooting videos, the moving objects in the scene are not located in each frame. The same, so if you directly use frames under different exposures for synthesis, multiple identical moving objects will appear in the synthesized HDR image, which is the so-called ghost

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