All‐Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging

Abstract

Quantitative phase imaging (QPI) is a label‐free computational imaging technique that provides optical path length information of specimens. In modern implementations, the quantitative phase image of an object is reconstructed digitally through numerical methods running in a computer, often using iterative algorithms. Here, a diffractive QPI network that can perform all‐optical phase recovery is demonstrated, and the quantitative phase image of an object is synthesized by converting the input phase information of a scene into intensity variations at the output plane. A diffractive QPI network is a specialized all‐optical processor designed to perform a quantitative phase‐to‐intensity transformation through passive diffractive surfaces that are spatially engineered using deep learning and image data. Forming a compact, all‐optical network that axially extends only ≈200–300λ, where λ is the illumination wavelength, this framework can replace traditional QPI systems and related digital computational burden with a set of passive transmissive layers. All‐optical diffractive QPI networks can potentially enable power‐efficient, high frame‐rate, and compact phase imaging systems that might be useful for various applications, including, e.g., microscopy and sensing.

Computation at the speed of light: metamaterials for all-optical calculations and neural networks

“…One example in particular that exploits the vectorial nature of EM waves is phase imaging, where the contrast of phase objects can be significantly increased, promoting new avenues of bioimaging and sensing 174 – 176 …”

Advances in Microfluidics for Single Red Blood Cell Analysis

“…3D Holographic Tomography being one of the most powerful 3D QPI methods advances further by combining various techniques into multimodal operations, integrating Raman imaging, Brillouin spectroscopy or fluorescence [33][34][35]. Artificial intelligence algorithms and machine learning approaches impact the system architecture improving measurement accuracy becoming the focus in 3D QPI systems [36][37][38][39][40][41][42].…”

“…grating Raman imaging, Brillouin spectroscopy or fluorescence [33][34][35]. Artificial intelligence algorithms and machine learning approaches impact the system architecture improving measurement accuracy becoming the focus in 3D QPI systems [36][37][38][39][40][41][42].…”

Seeing through unknown, random diffusers using all-optical diffractive networks

“…Computing using diffractive networks possesses the benefits of high speed, parallelism and low power consumption: the computational task of interest is completed while the incident light passes through passive thin diffractive layers at the speed of light, requiring no energy other than the illumination. This framework's success and capabilities were demonstrated numerically and experimentally by achieving various computational tasks, including object classification [24][25][26][27] , hologram reconstruction 28 , quantitative phase imaging 29 , privacy-preserving class-specific imaging 30 , logic operations 31,32 , universal linear transformations 33 , polarization processing 34 among others [35][36][37][38][39][40][41][42][43][44] . Diffractive networks can also process and shape the phase and amplitude of broadband input spectra to perform various tasks such as pulse shaping 45 , wavelength-division multiplexing 46 and single-pixel image classification 47 .…”