Mid-spatial-frequency (MSF) error on optical surfaces can do great harm to high-performance laser systems. A non-interferometric way of measuring it is phase retrieval, which has already proved its effectiveness in previous studies. However, the performance of phase retrieval is limited by its long-time iterative process and relies heavily on reliable initial solution. Therefore, in this paper, we put forward a method for fast measurement of MSF error, by introducing advanced deep learning technique into traditional computational imaging methods. Results show that the proposed method simultaneously gains an improvement on convergence speed and a reduction on residual error. The proposed method takes much fewer iterations to converge to the same error level, and has much smaller average residual error than that of the conventional algorithm in the numerical experiments.
In recent years, with the development of new materials, transparent objects are playing an increasingly important role in many fields, from industrial manufacturing to military technology. However, transparent objects sensing still remains a challenging problem in the area of computational imaging and optical engineering. As an indispensable part of 3-D modeling, transparent object sensing is a long-standing research topic, which aims to reconstruct the surface shape of a given transparent object using various kinds of measurement methods. In this paper, we put forward a new method for the sensing of such objects. Specifically, we focus on the sensing of thin transparent objects, including thin films and various kinds of nano-materials. The proposed method consists of two main steps. Firstly, we use a deep convolutional neural network to predict the original distribution of the objects from its recorded intensity pattern. Secondly, the predicted results are used as initial estimates, and the iterative projection phase retrieval algorithm is performed with the enhanced priors to obtain finer reconstruction results. The numerical experiment results turned out that, with the two steps, our method is able to reconstruct the surface shape of a given thin transparent object with a high speed and simple experimental setup. Moreover, the proposed method shows a new path of transparent object sensing with the combination of state-of-art deep learning technique and conventional computational imaging algorithm. It indicates that, following the same framework, the performance of such method can be significantly improved with more advanced hardware and software implementation.