Application of improved wavelet transform algorithm in image fusion

In order to overcome the defects of fuzzy detection, low recognition rate and poor real-time of traditional fusion methods used in precision-guided weapons systems, a new image fusion algorithm was proposed combining wavelet transform with Canny operator. Firstly, the source image was decomposed into 3 layers in vertical and horizontal directions, which are suitable for image reconstruction; then due to its own characteristics of the different frequency components, an unique fusion rule was used to change wavelet coefficients of images, that is, for the low frequency components, the weighted average fusion algorithm was adopted, and for the high-frequency components, wavelet coefficients were changed using Canny operator and the local area variance criteria method. Finally, images were reconstructed using the inverse wavelet transform for different components. Results show the improved method not only reduces the fuzziness of edge, highlights target color, gets better visual effects, but also makes computational efficiency high, real-time good, particularly can detect and recognize pretend targets. It has better theoretical research and application value.