Decoding Optical Data with Machine Learning

Abstract

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML‐based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science, and ML.

Artificial Intelligence in Classical and Quantum Photonics

“…After propagating a distance z, the measured intensity image is I(x, y) = |E(x, y, z)| 2 = H𝜙(x, y), where H is the forward operator that relates the phase 𝜙(x, y) at the origin z = 0 to the intensity image at distance z. To retrieve the optical phase it is then required to solve the inverse problem φ(x, y) = H inv I(x, y) (15) with φ denoting the estimate of the phase rather than the exact solution. The NN used in this experiment is based on a ResNet architecture.…”

“…In such cases, the employment of AI engines for optical data decoding is fundamental. [ 15 ] CNN models have been used to increase the quality of medical images, thus enhancing the accuracy of further traditional classification procedures and limiting the occurrence of incorrect diagnoses. [ 23 ] DL algorithms of this fashion turned out to be pivotal in diagnostics when imaging methods featuring a high signal‐to‐noise ratio or a complex data structure were involved, such as functional magnetic resonance imaging.…”

“…Artificial intelligence has also played a crucial role in medical diagnostic practices thanks to its ability to restrict the impact of human bias and increase the diagnosis reliability and accuracy. [14,15] Rather than replacing the role of medical doctors, AI algorithms proved to be very powerful in supporting them, providing image preselection, preprocessing, and classification while increasing time-effectiveness with low cost. [16][17][18][19][20][21][22] Moreover, optical data may reach high levels of complexity and dimensionality, leading to error-prone interpretations even in the case of experienced operators.…”

Deep Learning for Photonic Design and Analysis: Principles and Applications

“…Besides, the optical data generated from optical measurements are becoming more and more complicated. For instance, when applying optical spectroscopy to characterize various analytes (e.g., malignant tumor tissue and bacterial pathogens) in complex biological environments, it is challenging to extract the fingerprint due to the large spectral overlap from the common bonds in the analytes (Rickard et al, 2020;Fang et al, 2021). The traditional analysis methods are mainly based on the physical intuition and prior-experiences, which are time-consuming and susceptive to human error.…”

Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks

“…e research content of this paper mainly includes kinematics analysis and dynamics research of recon gurable robots, as well as neural network PID motion control and simulation research of recon gurable robots. In addition, during this period, with the promotion of high-bandwidth application technology, a lot of research work focused on the polarization, amplitude, and phase multiplexing of the optical eld, which also increased the data more or less transmission e ciency [4][5][6]. Due to the continuous breakthrough of various technologies, the channel capacity based on single-mode ber (SMF) is getting closer and closer to the Shannon capacity limit.…”