Learning-based real-time method to looking through scattering medium beyond the memory effect
arXiv:1910.11272 · doi:10.1364/OE.383911
Abstract
Strong scattering medium brings great difficulties to optical imaging, which is also a problem in medical imaging and many other fields. Optical memory effect makes it possible to image through strong random scattering medium. However, this method also has the limitation of limited angle field-of-view (FOV), which prevents it from being applied in practice. In this paper, a kind of practical convolutional neural network called PDSNet is proposed, which effectively breaks through the limitation of optical memory effect on FOV. Experiments is conducted to prove that the scattered pattern can be reconstructed accurately in real-time by PDSNet, and it is widely applicable to retrieve complex objects of random scales and different scattering media.
15 pages with 9 figures
References in corpus (9)
- Non-invasive real-time imaging through scattering layers and around corners via speckle correlations
- Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media
- Learning to see through multimode fibers
- Compressive Phase Retrieval via Generalized Approximate Message Passing
- Reference-less measurement of the transmission matrix of a highly scattering material using a DMD and phase retrieval techniques
- Real-time wavefront-shaping through scattering media by all optical feedback
- Single shot large field of view imaging with scattering media by spatial demultiplexing
- Imaging of objects through a thin scattering layer using a spectrally and spatially separated reference
- Deep Hybrid Scattering Image Learning