Infrared and Visible Image Fusion Using Wavelet Transform

In this article, we explore infrared and visible image fusion using wavelet transform methodology. This fusion approach implements a 2-level wavelet decomposition and incorporates multiple fusion rules to combine information from both image sources. The implementation typically involves using wavelet functions like Haar or Daubechies for decomposition, applying different fusion strategies to approximation and detail coefficients, and reconstructing the fused image through inverse wavelet transform. By merging these complementary image types, we achieve high-quality composite images that preserve critical information from both modalities. This technique finds significant applications across various domains including military surveillance, medical imaging, and security systems. Understanding the underlying algorithms, such as coefficient selection methods and fusion rule optimization, proves highly beneficial for both research and practical implementations in image processing workflows.