OP-FCNN: an optronic fully convolutional neural network for imaging through scattering media.

Imaging through scattering media is a classical inverse issue in computational imaging. In recent years, deep learning(DL) methods have excelled in speckle reconstruction by extracting the correlation of speckle patterns. However, high-performance DL-based speckle reconstruction also costs huge hardware computation and energy consumption.

Training optronic convolutional neural networks on an optical system through backpropagation algorithms.

The development of optical neural networks greatly slows the urgent demand of searching for fast computing approaches to solve big data processing. However, most optical neural networks following electronic training and optical inferencing do not really take full advantage of optical computing to reduce computational burden. Take the extensively used optronic convolutional neural networks (OPCNN) as an example, the convolutional operations still require vast computational operations in training stages on the computer.

Optronic convolutional neural networks of multi-layers with different functions executed in optics for image classification.

Although deeper convolutional neural networks (CNNs) generally obtain better performance on classification tasks, they incur higher computation costs. To address this problem, this study proposes the optronic convolutional neural network (OPCNN) in which all computation operations are executed in optics, and data transmission and control are executed in electronics. In OPCNN, we implement convolutional layers with multi input images by the lenslet 4f system, downsampling layers by optical-strided convolution and obtaining nonlinear activation by adjusting the camera's curve and fully connected layers by optical dot product.

Imaging through scattering media using semidefinite programming.

A clear image of an observed object may deteriorate into unrecognizable speckle when encountering heterogeneous scattering media, thus it is necessary to recover the object image from the speckle. A method combining least square and semidefinite programming is proposed, which can be used for imaging through scattering media. The proposed method consists of two main stages, that is, media scattering characteristics (SCs) estimation and image reconstruction.

Imaging through scattering media using speckle pattern classification based support vector regression.

Imaging through scattering media is a common practice in many applications of biomedical imaging. Object image would deteriorate into unrecognizable speckle pattern when scattering media is presented. Many methods have been investigated to reconstruct the object image when only speckle pattern is available.