Image Data Fusion Based on Wavelet Transform

In this paper, the image data fusion and evaluation based on wavelet transform are studied, the appropriate number of decomposition layers and wavelet types are selected, and four fusion rules are formulated for the decomposed high-frequency coefficient and low-frequency coefficient, and the feature-level image data fusion is completed. MATLAB software has been used to simulate the image fusion of plant set images and animal set images, and the fusion effect was evaluated by using color features, texture features, mean and variance indicators. The experimental results demonstrate that the image fusion effect is better when the fusion rule selects the largest high-frequency component and the average low-frequency component. The research on image fusion data based on wavelet transform can obtain high-quality fusion images based on multi-source images, which is of great significance to improve the utilization rate of image information and expand the application scenarios of images.