The implications of Compressive Sensing in signal processing

Compressive Sensing is an innovative platform for signal processing, which offers more practical methods to solve the issues of voluminous useless data generated during the series of processes associated with conventional signal processing paradigm, which are based on the traditional sampling theorem. The Compressive Sensing theory proposes that sparse signals can be successfully reconstructed from very few samples which are acquired at a much lower rate than the Nyquist rate. The theory is trying to combine the process of sampling, encoding and compressing into a simple and single step process. This concept has the potential to surpass the limits of Sampling Theorem and can perform better to deal with the related problems. The compressive sensing scenario is portrayed with this paper in an image processing environment. Aside from that, the paper puts an effort to analyze the processing and storage challenges associated with the conventional standards. The paper also proposes a prospective approach to resolve the issues concerned by using Compressive Sensing as an effective instrument. From this study and analysis it can be concluded that many of the challenges in the conventional methods can be defied contentedly with the h elp of Compressive Sensing.