Image Fusion Program Based on Compressed Sensing

Analysis of Image Fusion Technology Based on Compressed Sensing

Compressed Sensing (CS) is a signal processing technique that breaks through the traditional Nyquist sampling theorem. By leveraging signal sparsity, it enables efficient sampling and reconstruction through a small number of non-adaptive linear measurements. In the field of image fusion, this technology significantly reduces data acquisition requirements while maintaining fusion quality.

Core Principles Sparse Representation: Images exhibit sparsity in specific transform domains (such as wavelet or DCT), meaning most coefficients approach zero. Measurement Matrix Design: Random Gaussian matrices or Bernoulli matrices perform linear projection on original images to obtain low-dimensional measurements. Joint Optimization Fusion: Fuse measurements from multiple source images (using methods like weighted averaging or maximum value selection), then reconstruct the fused image through reconstruction algorithms.

MATLAB Implementation Key Points Measurement Process: Generate compressed data through sparse bases (like wavelet transform) and measurement matrices, significantly reducing transmission bandwidth requirements. Code implementation typically involves applying transform functions (e.g., wavedec2 for 2D wavelet decomposition) and matrix multiplication with random measurement matrices. Fusion Rules: Design adaptive rules in the measurement domain or sparse domain, such as coefficient selection based on regional energy, to preserve important features from multiple images. This can be implemented using region-based energy calculations and comparative selection algorithms. Reconstruction Algorithms: Commonly use Orthogonal Matching Pursuit (OMP) or Basis Pursuit (BP) algorithms to reconstruct images with high accuracy from fused measurements. MATLAB implementations often utilize optimization toolboxes or custom OMP/BP functions with iterative recovery processes.

Advantages and Challenges Advantages: Reduces hardware costs, suitable for resource-constrained scenarios (like remote sensing and medical imaging). Challenges: Reconstruction algorithms have high computational complexity, requiring trade-offs between fusion speed and accuracy; optimization of measurement matrices directly affects fusion effectiveness.

This technology provides a lightweight solution for multi-source image fusion, with future potential for combining deep learning to further optimize reconstruction efficiency through neural network-based recovery methods.