A Comprehensive Review on Compressive Sensing

Sparse sampling, also known as compressed sampling or compressed sensing (CS), is a new signal processing technique that samples the signal with considerably fewer samples than Nyquist's sampling theorem. Traditional sampling methods will not always be feasible, needing a strategy that overcomes the limitations of present approaches. Compressive sensing reduces complexity, speeds up processing, and improves storage efficiency. Compressive Sensing's reconstruction procedures ensure that the original signal is accurately restored. According to a recent study, CS outperforms other compression algorithms in terms of efficiency. This study aims to present an overview of existing Compressive Sensing approaches so that researchers can better understand existing limitations and seek to improve accuracy and precision.