Enhancing Reconstruction Quality in Block Compressed Sensing Through Directional Sparse Representation

In block compressed sensing (CS), sparse representation serves as a fundamental technique for signal reconstruction. However, to achieve superior reconstruction quality, incorporating directional characteristics becomes essential. Directional-enhanced sparse representation methods can significantly improve reconstruction accuracy and quality by leveraging directional information in signal decomposition. This approach finds broad applications in signal processing and image processing domains, particularly through algorithms like directional wavelet transforms or contourlet-based sparse coding. Implementation typically involves constructing directional dictionaries using mathematical transforms such as the discrete cosine transform (DCT) with orientation-adaptive blocks, or employing gradient-based operators to capture edge information. Key functions may include direction-aware thresholding operations and optimized measurement matrices that prioritize directional coherence. These enhancements contribute to improved model performance and precision while maintaining computational efficiency through block-based processing frameworks.