Image Reconstruction Algorithm Based on Compressive Sensing

This article introduces compressive sensing-based image reconstruction algorithms that are particularly suitable for beginners. These algorithms are widely applied in various domains such as medical image processing and robotic vision. The implementation typically involves solving underdetermined linear systems using optimization techniques like L1-minimization, where key functions may include sparse representation transformations (e.g., DCT or wavelet transforms) and reconstruction solvers (e.g., basis pursuit or iterative thresholding).

By studying these algorithms, you will gain deep insights into image processing fundamentals while enhancing your technical skills. The code structure usually consists of three main components: sensing matrix generation, sparse signal recovery, and image quality evaluation. Practical implementations often utilize MATLAB or Python with libraries such as CVX for convex optimization or scikit-learn for machine learning integration.

You can apply these algorithms to real-world projects to improve efficiency and quality, leveraging techniques like random sampling and nonlinear reconstruction. With broad application prospects and effective learning outcomes, we recommend studying and implementing these methods with hands-on coding exercises.