Aiming at the problems of low contrast, loss of detail and color distortion after fusion of near-infrared and color visible images, a new fusion algorithm of infrared and color visible images based on multi-scale transformation and adaptive pulse coupled neural network (PCNN) is proposed. Firstly, the visible color image is transformed into HSI (Hue Saturation Intensity) space. HSI color space contains three components: brightness, chroma and saturation, and these three components are not correlated with each other. Therefore, using this feature, the three components can be processed separately. The brightness component and the near-infrared image are transformed by multi-scale transformation, respectively. Tetrolet transform is chosen as the transformation method. After transformation, the low-frequency and high-frequency components are obtained, respectively. For the low-frequency components of the image, a fusion rule with the highest expectation is proposed. For the high-frequency components of the image, the threshold of the PCNN model is adjusted by the Gauss difference operator, and an adaptive PCNN model is proposed as the fusion rule. The fused image of the processed high and low frequency components through Tetrolet inverse transformation is used as a new brightness image. Then, the new brightness image and the original chromaticity and saturation components are mapped to RGB space, and the fused color image is obtained. In order to solve the problem of image smoothing and uneven illumination of the original image, a color and sharpness correction mechanism (CSC) is introduced to improve the quality of the fused image. In order to verify the effectiveness of the proposed method, five groups of near-infrared and color visible images with the resolution of 1 024×680 were selected for experiments and compared with four current efficient fusion methods and the method without color correction. The experimental results show that, compared with other image fusion algorithms, this method can retain the most details and textures with or without CSC color, and the visibility is greatly improved. At the same time, the results of this method have more details and textures under weak illumination conditions and have better contrast and good quality. Good color reproduction. It has great advantages in information retention, color restoration, image contrast and structural similarity.