Adaptive image fusion method based on chromaticity coordinates

Abstract

The present invention discloses an adaptive image fusion method based on chromaticity coordinates. The realization processes of the method comprise firstly obtaining the chromaticity coordinate mean values of a foreground image and a background image; secondly, modifying the R, G and B values of each pixel in the foreground image according to the chromaticity coordinate mean values of the foreground image and the background image; then utilizing a Gauss fuzzy algorithm to obtain the weighting coefficients of the edges of the foreground image, and carrying out the gradient weighted fusion on the foreground image and the background image at the foreground edges. According to the algorithm given out by the present invention, the brightness and the chromaticity of the foreground image can be adjusted adaptively according to the background image, on the condition that the foreground and background colors and brightness have greater difference, the situations that the synthetic images are clear, and the foreground color and brightness do not distort, can be kept, and the situation that the edge synthesis of the foreground image is excessively natural can be guaranteed by utilizing a Gauss fuzzy method to obtain the weighting coefficients of the edges of the foreground image and utilizing the gradient weighted fusion processing. Relative to a Poisson image fusion algorithm, the method of the present invention has a lower calculation complexity and can be widely used in some mobile terminal programs of limited hardware resources.

Description

technical field [0001] The invention relates to an adaptive image fusion method based on chromaticity coordinates, belonging to the technical field of image processing. Background technique [0002] With the rapid development of computer graphics, digital image processing technology has been greatly improved, making the seamless fusion of different images an important method for us to obtain new images. This method greatly improves the efficiency of image acquisition on the basis of reusing existing images. The requirement of seamless image editing is to seamlessly blend one or more parts from different images into another background image to obtain a new image without obvious artifacts. Today, image fusion has become a very important image analysis and computer vision technology. Image fusion has a wide range of applications in automatic target recognition, computer vision, remote sensing, robotics, medical image processing, and military applications. Image fusion is the...

AI Technical Summary

Problems solved by technology

[0006] 1) When the conventional image fusion algorithm has a large difference in foreground and background color and brightness, there will be problems such as blurred composite image, blurred edge of foreground image, distortion of foreground color, and large brightness deviation.

[0007] 2) The computational complexity of conventional image fusion algorithms is still very high, and some applications with limited hardware resources, such as mobile phone client programs, cannot be directly applied

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