Curvelet Toolbox Based Infrared and Visible Light Image Fusion Development

I have developed an infrared and visible light image fusion algorithm using the Curvelet Toolbox. This implementation employs curvelet transform - a multi-scale geometric analysis tool that effectively captures directional features in images. The code processes both infrared and visible light images through curvelet decomposition, applies fusion rules to combine coefficient matrices (typically using maximum selection or weighted average methods for different frequency bands), and reconstructs the fused image through inverse curvelet transform. The resulting fused image preserves thermal signatures from infrared data while maintaining textural details from visible light, providing comprehensive information for applications such as target detection, image enhancement, and medical diagnostics. The implementation efficiently handles multi-scale directional features through the Curvelet Toolbox's built-in functions for forward/inverse transforms and coefficient manipulation, achieving satisfactory fusion results with clear visual improvements. I believe this code will contribute valuable insights and practical solutions to the image processing research community.