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Deeply coded aperture for lensless imaging
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DOI：10.1364/OL.390810.png)
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Abstract
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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. (C) 2020 Optical Society of America