toolbox_signal - Curvelet and Ridgelet Transform Source Code Implementation

In this document, we explore the source code implementations for curvelet transform and ridgelet transform. The curvelet transform serves as an effective technique for processing highly complex datasets, such as medical images or seismic data, utilizing directional sensitivity and multi-scale decomposition. The ridgelet transform represents a widely adopted method in image processing and compression that effectively separates features across different orientations and scales. Our discussion covers the fundamental principles of these techniques and provides comprehensive source code implementations. We examine key algorithmic components including directional filtering, radon transform integration for ridgelet implementation, and frequency partitioning strategies for curvelet decomposition. Additionally, we investigate code optimization approaches to enhance computational performance through algorithmic refinements and memory management techniques. The implementation guidelines include practical applications across diverse datasets and problem domains, with specific attention to parameter tuning and transformation coefficient analysis.