Image fusion method

Abstract

The embodiment of the invention provides an image fusion method. The method comprises the following steps: acquiring a visible light image and an infrared light image registered with the visible light image; obtaining first brightness information in the visible light image and second brightness information in the infrared light image; obtaining a first low-frequency component and a first high-frequency component of the first brightness information and a second low-frequency component and a second high-frequency component of the second brightness information; compensating the second low-frequency component by using residual data between the first low-frequency component and the second low-frequency component to obtain a third low-frequency component; performing weighted fusion on the first low-frequency component and the third low-frequency component by using the fusion weight matrix to obtain a fused low-frequency component; obtaining fused brightness information by using the fused low-frequency component, the first high-frequency component and the second high-frequency component; and obtaining a fused image according to the fused brightness information and the color information of the visible light image. According to the embodiment of the invention, on the basis of ensuring the color authenticity, the signal-to-noise ratio and detail display effect of the fused image are improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image fusion method. Background technique [0002] At present, there are many image fusion schemes in the field of image processing, most of which use a single-camera spectroscopic structure or a dual-camera structure to obtain information of different spectral bands, basically focusing on visible light images and non-visible light images, and then combining the advantages of the two Perform image fusion to get better image effects. [0003] Although the fusion algorithms of each scheme are quite different, the ultimate goal they want to achieve is indeed the same, that is, to improve the image effect under low illumination, which is reflected in many aspects such as signal-to-noise ratio, color, and detail outline. However, objects have different reflection and absorption characteristics for different spectra, so the brightness and texture performance of the same ob...

AI Technical Summary

Problems solved by technology

However, objects have different reflection and absorption characteristics for different spectra, so the brightness and texture performance of the same object in visible light images and infrared images may vary greatly, especially in low-light environments

For the monitoring field, under reasonable lighting conditions, the signal-to-noise ratio of non-visible light images and the texture of the scene are often better, but non-visible light images do not have real color information, so choosing too much infrared information can easily lead to color distortion, Unnatural textures, etc.

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