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Synthetic aperture imaging systems can be applied to equivalently get high-resolution images of traditional monolithic primary mirror systems with less weight and costs. Spatial modulation diversity (SMD), a newly developed post-processing technology applicable for such synthetic aperture systems, is very sensitive to Gaussian noise, which greatly limits its further application. In this paper, we propose an improved SMD strategy by introducing the deep denoising convolutional neural networks (DnCNN) into the image preprocessing to improve the robustness of SMD. Results of the numerical simulations demonstrate that the strategy proposed exhibits superior performance compared to the traditional SMD technique in terms of both the root-mean-square error (RMSE) of phase estimates and the structural similarity (SSIM) of image reconstruction. In view of the superiority and robustness, the method we proposed may have important application prospects in multi-aperture imaging systems. [ABSTRACT FROM AUTHOR]

The number of publications of aperture-synthesis images based on optical long-baseline interferometry measurements has recently increased due to easier access to visible and infrared interferometers. The interferometry technique has now reached a technical maturity level that opens new avenues for numerous astrophysical topics requiring milli-arcsecond model-independent imaging. In writing this paper our motivation was twofold: (1) review and publicize emblematic excerpts of the impressive corpus accumulated in the field of optical interferometry image reconstruction; (2) discuss future prospects for this technique by selecting four representative astrophysical science cases in order to review the potential benefits of using optical long-baseline interferometers. For this second goal we have simulated interferometric data from those selected astrophysical environments and used state-of-the-art codes to provide the reconstructed images that are reachable with current or soon-to-be facilities. The image-reconstruction process was 'blind' in the sense that reconstructors had no knowledge of the input brightness distributions. We discuss the impact of optical interferometry in those four astrophysical fields. We show that image-reconstruction software successfully provides accurate morphological information on a variety of astrophysical topics and review the current strengths and weaknesses of such reconstructions. We investigate how to improve image reconstruction and the quality of the image possibly by upgrading the current facilities. We finally argue that optical interferometers and their corresponding instrumentation, existing or to come, with six to ten telescopes, should be well suited to provide images of complex sceneries. [ABSTRACT FROM AUTHOR]