Research On Image Reconstruction Algorithm Based On Compressed Sensin

Communication services have developed from simple voice to the multi-service mix with the development of technology in the 21st century,which makes the amount of information transmission continue to increase.It’s an an urgent problem to find a new sampling method because Nyquist sampling theorem’s ability to deal with data is difficult to meet people’s needs.The compressed sensing can sample the signal at a frequency lower than the Nyquist frequency,and it can not only greatly reduce the computational complexity of the sampling phase,but also complete signal reconstruction in high probability.Reconstruction algorithm is an important part of compression sensing.The traditional reconstruction algorithm focuses on consuming more time for better effect,which makes it difficult to process large scale data.This paper improved the traditional orthogonal matching pursuit algorithm(OMP)in order to solve the contradiction of reconstruction effect and complexity of algorithm in signal construction of compressed sensing.Based on the idea of subspace tracking algorithm,the conjugate gradient method is used to estimate the signal instead of ordinary least square.The improved algorithm can be carried out by simulation and the results are good in reconstruction quality and reconstruction time.This paper makes simulation and analyze the influence of noise and fading on signal reconstruction,and the simulations proved that the improved algorithm has more advantages than OMP algorithm in case of noise or missing signal.