The Phase Recovery Research Based On Sparsity

The phase information typically carry important information about the structure of the object, the current detector can only receive the data object in some diffracted light ray irradiation, it will lead to the loss of phase information of the object. Reference herein to GS phase restoration algorithm, hybrid input-output algorithm(HIO) and the basic principles of strength transfer equation solving method were analyzed, mainly analyzes the basic principles of GS algorithm, HIO algorithm, strength transfer equation method for solving the phase problem and the basic rehabilitation Step through the experimental analysis of the influence of the initial phase of the recovery results and compare three-phase restoration algorithm final phase of restoration effect.Sparse representation theory is the basis of sparsity recovery phase; therefore, first it is introduced. Then, the compressed sensing theory is introduced. In particular, the theory of compressed sensing and sparse decomposition algorithm focuses on reconstruction, last experiment compares several compressed sensing algorithm effect, and the experimental results are analyzed.The last of the compressed sensing algorithm and phase recovery algorithm integrated first phase of the information signal is removed and then use compressed sensing sparse sampling algorithm no sampling data phase information, then the introduction of the iterative reconstruction algorithm in a sparse phase recovery algorithm, the use of such non-phase transformation algorithm in a sparse sampling data obtained can be reconstructed to the original signal. Finally, the effect of improving the algorithm experiments, and improved algorithm results were analyzed.