Infrared and Visible Image Fusion Using Wavelet Analysis Theory

This project implements infrared and visible image fusion using wavelet analysis theory. The fusion method leverages wavelet transform principles to combine infrared and visible light images, resulting in richer image information. Wavelet analysis is a mathematical tool that decomposes signals or images into sub-signals or sub-images at different frequency bands. In implementation, the algorithm first performs wavelet decomposition on both infrared and visible images using functions like wavedec2() in MATLAB or PyWavelets in Python. This separates each image into high-frequency components (containing detailed information like edges and textures) and low-frequency components (representing the overall structure). The fusion process typically involves: 1. Applying discrete wavelet transform (DWT) to both input images 2. Implementing fusion rules: high-frequency coefficients are often combined using maximum selection or weighted average methods, while low-frequency components may use averaging or region-based fusion 3. Performing inverse wavelet transform using waverec2() to reconstruct the fused image Key algorithmic considerations include selecting appropriate wavelet bases (such as Haar, Daubechies, or Symlets) and determining optimal fusion rules for different frequency bands. The resulting fused image provides enhanced clarity and detail by preserving thermal information from infrared images and texture details from visible light images. This fusion methodology has wide applications in infrared and visible image processing domains, significantly improving image quality and resolution while providing comprehensive information for analysis and decision-making tasks.