A Fusion Method of Infrared Image and Low Light Image

Abstract

The invention relates to a new fusion method of infrared images and low-light images. The process of the method is: first, perform multi-scale and multi-directional NSCT transformation decomposition on the infrared images and low-light images, and obtain the low-frequency subbands and low-frequency subbands of the infrared images respectively. High-frequency subbands and low-frequency subbands and high-frequency subbands of low-light images; in the low-frequency subbands, a smoothing linear filter is first used to preprocess the low-frequency coefficients. The smoothing linear filter is located at the weight ratio of the mask center. The weights of the four sides and corners are large, and the weights located at the four corners are the smallest. The output response of this smooth linear filter is the weighted average within the template, and is fused using a method based on a combination of local energy ratio and local energy weighting; in high-frequency sub-areas On the belt, first use the Laplacian operator based on the second-order differential to preprocess the high-frequency coefficients. The Laplacian filter is an isotropic filter. Finally, NSCT multi-resolution reconstruction is performed on the fusion coefficients. Get the fused image.

Description

technical field [0001] The invention belongs to the technical field of image fusion, and in particular relates to a fusion method of an infrared image and a low-light image. Background technique [0002] In the field of image fusion, different types of sensors can be used to provide different types of image information for the same detection area, so as to obtain a more accurate image of the same scene that meets the detection needs. In night or low-light detection occasions, infrared sensors and low-light sensors are often used together. The infrared image receives the infrared information of the target, and the low-frequency information of the target is more prominent; the signal-to-noise ratio of the low-light image is high, and the detailed information provided is more abundant. . Through the image fusion algorithm, the characteristics of each sensor can be used to obtain more ideal target indication scene information. [0003] Traditional image fusion algorithms have ...

AI Technical Summary

Problems solved by technology

[0003] Traditional image fusion algorithms have disadvantages such as poor contrast between the target and the background, unobvious target features, and weak detail information.

For example, Qian Xiaoyan et al. (Qian Xiaoyan, Han Lei, Wang Bangfeng. Fast fusion algorithm for infrared and visible light images [J]. Journal of Computer-Aided Design and Graphics, 2011,23(7):1211-1216.) proposed Combining local histogram equalization to enhance infrared and low-light images, and using median filtering to denoise, the fusion is fast and the details are prominent, but the target is not obvious; Xing Suxia et al. (Xing Suxia, Xiao Hongbing, Chen Tianhua, etc. based on target extraction and Research on NSCT image fusion technology [J]. Optoelectronics Laser, 2013 (3): 583-588.) proposed to use information entropy to segment infrared images to extract targets, and use NSCT algorithm to enhance the low-frequency components of low-light images for fusion. Good, but the details of the image are slightly weaker during its enhancement process

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