No abstract

We present a method for single-shot three-dimensional imaging through scattering media with a threedimensional memory effect. In the proposed computational process, a captured speckle image is twodimensionally correlated with different scales, and the object is three-dimensionally recovered with threedimensional phase retrieval. Our method was experimentally demonstrated with a lensless setup and was compared with a multi-shot approach used in our previous work [Y. Okamoto, et al., Opt. Lett. 44, 2526-2529 (2019)].

In this Letter, we present a method for jointly designing a coded aperture and a convolutional neural network for reconstructing an object from a single-shot lensless measurement. The coded aperture and the reconstruction network are connected with a deep learning framework in which the coded aperture is placed as a first convolutional layer. Our co-optimization method was experimentally demonstrated with a fully convolutional network, and its performance was compared to a coded aperture with a modified uniformly redundant array.