Abstract
To solve the fusion problem of visible and infrared images, based on image fusion algorithm such as region fusion, wavelet transform, spatial frequency, Laplasse Pyramid and principal component analysis, the quality evaluation index of image fusion was defined. Then, curve-let transform was used to replace the wavelet change to express the superiority of the curve. It integrated the intensity channel and the infrared image, and then transformed it to the original space to get the fused color image. Finally, two groups of images at different time intervals were used to carry out experiments, and the images obtained after fusion were compared with the images obtained by the first five algorithms, and the quality was evaluated. The experiment showed that the image fusion algorithm based on curve-let transform had good performance, and it can well integrate the information of visible and infrared images. It is concluded that the image fusion algorithm based on curve-let change is a feasible multi-sensor image fusion algorithm based on multi-resolution analysis.
Highlights
After years of development, the image sensor and the computing ability of computer is greatly improved
This paper deals with the fusion of visible light color and infrared images
The experiment showed that the image fusion algorithm based on curve-let transform had good performance
Summary
The image sensor and the computing ability of computer is greatly improved. The information obtained by a single sensor is always limited and cannot meet the requirements in all cases. The spectral range accepted by the commonly used visible light images is limited to the range identified by the human eye, and the image information is far from enough. If at night or in the case of severe light, the scene information obtained by visible light is less. In the case of insufficient light, it has great superiority to the recognition of the target. If the visible light image and the infrared image information are fused together, the information in the scene is more comprehensive
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