Abstract

We present a method for less-invasive imaging through scattering media. We use an image-to-image translation, which is called a cycle generative adversarial network (CycleGAN), based on semi-supervised learning with an unlabeled dataset. Our method was experimentally demonstrated by reconstructing object images displayed on a spatial light modulator between diffusers. In the demonstration, CycleGAN was trained with captured images and object candidate images that were not used for image capturing through the diffusers and were not paired with the captured images.

© 2020 Optical Society of America

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