Image adaptive recovery based on compressive sensing and genetic algorithm

Aiming at the characters of compressive sensing and genetic algorithm (GA), an adaptive image recovery method is proposed in this paper. Compressive sensing 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 which is broadly applied in compressive imaging. GA is global numerical-optimization method which works well in the problem of optimization. Therefore, the new method proposed in this paper combines advantages of compressive sensing and GA which can adaptively look for optimal solution to ensure the best recovery performance. The experiments show that the new method not only has better recovery quality and higher PSNRs, but also can effectively avoid the premature convergence problem and achieve optimization steadily.