Multiscale image natural color fusion method and device based on over-segmentation and optimization

Abstract

The invention provides a multiscale image natural color fusion method based on over-segmentation and optimization, which comprises the steps of: acquiring a grayscale fusion image; selecting a reference image, and carrying out over-segmentation treatment on the grayscale fusion image and the reference image; transforming the images from a RGB (Red Green Blue) color space to a 1alpha beta color space for area transmission; transmitting color information in an over-segmentation area in the reference image to the grayscale fusion image; determining a special area according to a point with the brightness higher than the integral image brightness in an infrared image; and transforming from the 1alpha beta color space to the RGB color space so as to acquire a fusion image with natural colors. The invention provides a multiscale image natural color fusion device based on over-segmentation and optimization, which comprises a transformation module, an over-segmentation module, an area transmission module, a color transmission module and a color fusion module. The method and device of the invention can improve the fusion effect of a multi-source grayscale image, enhance information resolving power, alleviate asthenopia of an observer and enhance sustainable observing ability.

Description

Technical field [0001] The invention relates to the field of image processing, in particular to a method and device for natural color fusion of multi-scale images based on over-segmentation and optimization. Background technique [0002] Multi-sensor image fusion is to use a certain algorithm to synthesize the images or image sequence information about a specific scene obtained by two or more sensors to generate a new image about the scene. The new image obtained by the fusion has higher credibility, less blur and better understandability, and is more suitable for human vision or computer detection, classification, recognition, understanding and other processing, thereby improving the detection of the image detection system And recognition ability. [0003] The methods of gray image fusion mainly include: [0004] 1) Simple pixel fusion: directly average or weight the pixels of the source image, typically generalized linear weighting, principal component (PCA) methods, etc. [0005]...

AI Technical Summary

Problems solved by technology

[0013] The current image color fusion method still has insufficient visual detail information, and does not give the fused grayscale image a natural color. The observer is prone to fatigue when watching the image, which is not suitable for long-term observation.

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