Image Fusion Program Based on Wavelet Transform and Compressed Sensing Theory

This document provides a detailed description of an image fusion program developed using wavelet transform and compressed sensing theory:

In this program, wavelet transform is employed to process input images. Wavelet transform serves as a mathematical tool that decomposes images into different frequency components. These components include low-frequency components containing the primary image information and high-frequency components capturing image details and textures. The implementation utilizes discrete wavelet transform (DWT) functions to perform multi-level decomposition, typically using wavelet families like Daubechies or Symlets through functions such as wavedec2() for 2D signal processing.

Additionally, compressed sensing theory is integrated into the program. Compressed sensing represents an advanced signal processing technique that enables data compression while maintaining data integrity. The implementation incorporates compressed sensing algorithms through sparse representation and reconstruction methods, utilizing optimization techniques like L1-norm minimization for signal recovery from fewer measurements than required by traditional Nyquist sampling.

Thus, this program combines wavelet transform and compressed sensing theory to achieve high-quality image fusion through frequency-domain decomposition followed by optimized reconstruction, ensuring preservation of both structural information and detailed features in the final fused image.