CS Compressed Sensing: A Beginner's Tutorial with Code Implementations

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Resource Overview

This introductory teaching code demonstrates CS compressed sensing reconstruction using the Orthogonal Matching Pursuit (OMP) algorithm. The fully annotated implementation covers 1D signal and 2D image reconstruction with DCT and wavelet sparsification techniques, featuring both column-scanning and block-based OMP reconstruction methods.

Detailed Documentation

This beginner-friendly teaching code demonstrates compressed sensing reconstruction using the OMP algorithm. The implementation includes comprehensive code comments and covers both 1D signal and 2D image reconstruction scenarios. For sparse representation, we employ Discrete Cosine Transform (DCT) and wavelet transform techniques. The OMP reconstruction process implements two distinct approaches: column-scanning method for sequential processing and block-based method for handling larger datasets efficiently. These implementations feature optimized matrix operations and iteration control mechanisms to enhance computational efficiency. The modular code structure improves readability and maintainability, making it easier for students to understand key compressed sensing concepts through practical examples. Each function includes parameter descriptions and algorithm flow explanations to facilitate learning.

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