Program Description: EAGER: Electronics, Photonics, and Magnetic Devices
Imaging through scattering and diffusive media such as fog, clouds or human tissue has been an important problem for many decades. Without an exception, all the previous methods are based on, at their core, digital computers, such that the signals are first detected by a device and then processed using digital computers to reconstruct the diffuser-distorted images. There is an important and pressing need for a new generation of optical devices that can see, detect and quantify target objects through for example human tissues, walls, packages, clouds, fogs, etc., at the speed of light and without using any power-hungry digital computation. This unique capability, once fully demonstrated and developed, might open various new applications in autonomous systems, biomedical imaging, astronomy, astrophysics, atmospheric sciences, security, robotics, and many other fields.
In this proposal, a computer-free, all-optical device that will see through unknown diffusers at the speed of light, without the need for any digital computation device will be developed. Unlike previous digital approaches that utilized computers to reconstruct an image of the input object behind unknown diffusers, a passive device will be created using a set of diffractive surfaces/layers to all-optically reconstruct the image of an unknown object as the diffuser-distorted input signals diffract through successive trained diffractive layers, i.e., the image reconstruction will be processed at the speed of light through this device. Each diffractive surface of a given device designed will have thousands of diffractive features (termed as neurons), where the individual phase values of these neurons will be adjusted in the training phase through error back-propagation, by minimizing a customized loss function between the ground truth image and the diffracted pattern at the output field-of-view. After this deep learning-based design of these diffractive layers, the resulting passive device will be fabricated to form a physical diffractive optical network that is positioned between an unknown diffuser and the output/image plane. As the input object light passes through an unknown diffuser, the scattered light will be collected by the trained diffractive device to passively reconstruct the distorted image. The success of this diffractive device will be demonstrated in 0.1-3 THz frequency band. Unlike other devices, the proposed diffractive image reconstruction device operates at the speed of light and does not require any power except for the illumination light. This all-optical image reconstruction that will be achieved by passive diffractive layers will enable to see objects through unknown diffusers and present an extremely low power device compared with existing deep learning-based or iterative image reconstruction methods implemented in computers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
