Information Fusion of Images Using Wavelet Transform Technology

In this article, we analyze how wavelet transform technology can be employed for image information fusion. This technique allows us to combine different medical images to obtain more comprehensive and clearer diagnostic information. Through detailed explanations and practical examples, we will help readers better understand the principles and methods of medical image fusion technology, providing them with deeper insights into the implementation process. The article explores key algorithmic steps including wavelet decomposition using functions like wavedec2() in MATLAB, which separates images into approximate and detailed coefficients across multiple resolution levels. We also discuss fusion rules implementation - such as selecting maximum coefficients or weighted averaging for different frequency bands - and wavelet reconstruction using waverec2() to generate the final fused image. Additionally, we examine the technology's applications in medical fields and introduce recent research achievements and future development directions. After reading this article, readers will gain thorough understanding of medical image fusion technology and be able to apply it to practical medical image processing tasks through proper code implementation and parameter optimization.