Phase retrieval is of great significance in the fields of medical imaging and computational holography. To solve the problem more efficiently, this paper presents a phase recovery network with two modes of operation based on the convolution neural network, which not only can get persistent model by training data set, but also can build a special loss function to recover the unknown signal in a self-optimized way without data set in the case of Gaussian measurement model. Comparison of the simulation results show that the network is able to obtain better results with fewer measurements than the existing phase recovery algorithms.
Phase unwrapping aims to reconstruct the absolute phase from its values mod 2π, which is a key problem in many non-contact optical metrologies and is still a challenging issue in the presence of noise. We present a robust phase unwrapping method with two steps based on local denoising and the accumulation of residual map (ARM). In the first step, based on the local polynomial approximations and intersection of confidence intervals, we adopt a sine–cosine filtering and iteration algorithm to denoise the wrapped phase, which uses the periodicity of the trigonometric function to avoid the influence of phase jumps effectively. In the second step, the denoised wrapped phase is unwrapped by the improved ARMs method, in which we modify the edge regular term to avoid over-fitting and maintain edge information. The algorithm is tested with a set of numerical simulations under various noise levels, and the experimental results confirm its effectiveness and robustness.
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