Image Fusion Program Based on Compressive Sensing

The image fusion program based on compressive sensing represents an advanced image processing technique that utilizes compressive sensing theory to achieve image fusion. This technology significantly reduces computational requirements for image processing while maintaining image details and achieving simultaneous image compression. MATLAB serves as a widely adopted scientific computing tool ideal for implementing compressive sensing-based image fusion programs. The platform offers numerous toolboxes, including specialized image processing toolboxes, which greatly facilitate the development of compressive sensing-based fusion algorithms. The implementation typically involves key MATLAB functions such as sparse representation using wavelet transforms (wavedec2/waverec2), measurement matrix generation (randn for random matrices), and reconstruction algorithms like Orthogonal Matching Pursuit (OMP) or Basis Pursuit. The core algorithm workflow generally includes: sparse transformation of source images, random measurement acquisition, joint sparse reconstruction, and inverse transformation to obtain the fused image. This approach ensures that critical image information is preserved while reducing data acquisition and processing overhead. Therefore, compressive sensing-based image fusion programs constitute highly valuable technology with broad applications across multiple domains, including medical image processing, satellite imagery analysis, remote sensing, and surveillance systems. The MATLAB implementation provides researchers with efficient prototyping capabilities and robust performance validation for real-world image fusion scenarios.