Application of compressed sensing on images via BCH measurement matrices

Compressed sensing is an emerging technique of signal compression domain. The signal can be recovered from it´s under sampled measurements using optimization techniques. The only condition is the signal should be sparse in some domain. This technique finds its application in many fields like medical imaging, UWB communication, voice compression etc. One of the important parameter of compressed sensing frame work is the K × N measurement matrix. Recent Techniques have been developed to use deterministic sensing matrices instead of traditional Random Sensing matrices. This paper reviews the concepts of compressed sensing and applies the technique on images using the deterministic compressed sensing matrix formed using BCH code vectors. The motivation behind the work is to provide a frame work so that the concept can be applied on real time signal processing.