Object-independent tilt detection for optical sparse aperture system with large-scale piston error via deep convolution neural network

Abstract

The optical sparse aperture technique can improve the imaging resolution significantly under the ideal co-phase condition. However, the position deviation between different sub-apertures leads to notorious co-phase errors, seriously impacting the image quality. While the position deviation arises in practical applications, it is difficult to detect the errors in real-time for traditional iterative algorithms because of their narrow detection range and long-time iteration process. The deep neural network has shown its potential in optical information process, and it has some attempts in the detection of piston error. However, all existing deep learning-based methods just focus on the detection of piston error with the weak or corrected tilt error, which is not in line with reality. Here we implement the deep convolutional neural network to detect tilt error with large-scale piston error, and compare the detection performance of two kinds of network, one takes the point spread function as input while the other takes the phase diversity features as the input. The detection ability and generalization capability of network are verified under single wavelength, broadband light and turbulence aberration in simulation. The object-independent of tilt error detection ability is offered because the phase diversity features and point spread function are all unrelated to the object. In addition, the cyclic correction strategy is carried out to improve the generalization performance facing the larger errors. As a result, the deep learning-based method can detect the tilt error accurately with fast calculation, and the trained network is hopeful for the real-time correction with cyclic correction strategy.