Sparse Representation-Based Image Fusion

The sparse representation-based image fusion algorithm is a method that leverages image sparsity for image fusion. Sparse representation refers to the phenomenon where a vector's expression under a specific dictionary is sparse, meaning only a small subset of coefficients are non-zero. This algorithm is particularly effective for synthesizing panoramic images while preserving the detailed information and edge characteristics from the original images without distortion. To achieve this, the algorithm first decomposes the source images that require fusion, then performs image fusion using sparse representation techniques. The implementation typically involves creating an over-complete dictionary (often using methods like K-SVD or DCT) and solving optimization problems through techniques such as Orthogonal Matching Pursuit (OMP) or L1-norm minimization. This approach enables the fusion of multiple images into a single high-quality panoramic image while maintaining critical image details and edge integrity.