MATLAB Implementation of Imaging Algorithms: RD, RMA, and CS

This project presents MATLAB implementations of three key imaging algorithms: RD (Range Doppler), RMA (Range Migration Algorithm), and CS (Compressed Sensing). These algorithms are fundamental to image processing applications, where RD algorithm specializes in image denoising through frequency domain filtering techniques, RMA algorithm focuses on image restoration using phase compensation and Stolt interpolation methods, and CS algorithm enables efficient image compression via sparse signal reconstruction approaches. The implementation utilizes MATLAB's powerful signal processing toolbox and matrix operations to achieve optimal performance. Key functions employed include fft2/ifft2 for Fourier transformations, imfilter for spatial domain operations, and optimization solvers for compressed sensing reconstruction. The code demonstrates practical applications of these algorithms by processing digital images through different stages: noise reduction using RD's Wiener filtering approach, blur removal via RMA's wavefront curvature correction, and data compression through CS's l1-minimization techniques. Through systematic application of these algorithms, the program aims to significantly enhance image processing outcomes by improving visual clarity, restoring image fidelity, and maintaining critical image information while reducing storage requirements. Each algorithm includes parameter tuning options to adapt to different image characteristics and quality requirements, making this implementation suitable for both educational and research purposes in digital image processing.