Image recovery based on compressive sensing and Curvelet transform via ROMP

Conventional signal sampling requires that a signal must sample at least two times faster than the signal bandwidth to avoid losing information. But in recent years, an emerging theory of “compressive sensing” which shows that it can capture and represent compressible signal at a rate below the Nyquist rate, and it is possible to reconstruct signals accurately and sometimes even exactly from far fewer data than what is usually considered necessary via using an optimization process. In this paper, we present a new image recovery approach based on compressive sensing via Regularized Orthogonal Matching Pursuit algorithm in Curvelet transform domain. The experiment results show that the reconstructed image has better quality and higher PSNR, and the new method is faster and more stable than the Orthogonal Matching Pursuit algorithm.