The Research Of In-line Digital Holography Image Reconstruction Based On Total Generaliazed Variation

In-line digital holography has been widely used in various fields,but in the process of traditional hologram reconstruction,the quality of reconstructed image suffers from the superposition of twin-image contamination.The phase retrieval algorithm is used to recover the lost phase information with the help of the measured hologram intensity,which can eliminate the influence of twin-image image completely.In this paper,we focus on the research of phase retrieval reconstruction algorithm,which based on in-line digital holography.The main contents are as follows:Firstly,a resistance to poisson noise pollution phase retrieval algorithm based on in-line digital holography is proposed for pure phase object.The negative-log poisson likehood function is used for data fidelity term and second order total generaliazed variation(TGV)is used for the regularization term of the model.Experimental results show that the proposed algorithm can eliminate the interference of the twin-image in the case of Poisson noise with different intensities,making the image reconstruction accurately.Secondly,a new in-line digital holography reconstruction algorithm is proposed,which based on the second order total generaliazed variation and total variation(TV)hybrid regularization.By using TGV regularization and TV regularization in the proposed algorithm,which can constraint spatial amplitude and phase distribution of the reconstructed data separately.Moreover,the complex image can be represented in two different forms.The alternating direction method of multipliers(ADMM)is utilized for solving the corresponding optimization problem.Experimental results demonstrate the effectiveness and robustness of the proposed algorithm,in addition,even at a low oversampling rate,the proposed algorithm can achieve better image reconstruction quality.Finally,on the basis of in-line digital holography,we proposed a new image reconstruction algorithm based on the second order total generaliazed variation.For the real and pure phase object,the second order total generaliazed variation regularization can constrain the reconstructed data.For the complex-valued image,the second order total generaliazed variation regularization can constrain the spatial amplitude and phase distributions of the reconstructed data separately.Experimental results show that TGV can eliminate the staircase effect,and can preserve the edge and detail information of the image as much as possible.The reconstructed image is more real when the second order total generaliazed variation is used for the regularization term.Thus,reflect the effectiveness of the algorithm.