ReviewLight, science & applications2022
Deep learning in optical metrology: a review.
Review in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 85 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
85 citing papers in PubMed.
- Hybrid deep reconstruction for vignetting-free upconversion imaging through scattering in epsilon-near-zero materials.Light, science & applications · 2026Article
- Recent Progress in Optical Frequency-Domain Reflectometry: Performance Enhancement, Emerging Applications and Machine Learning Integration.Sensors (Basel, Switzerland) · 2026Review
- High-Precision Rotation Axis Calibration of Line-Structured Light Measurement System Using a Stepped Cylinder.Sensors (Basel, Switzerland) · 2026Article
- Wavefront Line-Scan Imaging Via a Single-Pixel Detector.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Towards theoretical foundation for high-precision fringe projection profilometry system design and optimization.Light, science & applications · 2026Article
- Nanometre-precision terahertz interferometry for battery electrode metrology.Nature communications · 2026Article
- Accurate and fast event-based shape measurement of mixed reflectance scenes.Nature communications · 2026Article
- Super-resolution image projection over an extended depth of field using a diffractive decoder.Light, science & applications · 2026Article
- On the precision models of fringe projection profilometry: unification, simplification and connection.Light, science & applications · 2026Article
- Uncovering advanced transfer learning strategies for deep neural networks in natural language processing.Scientific reports · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Single-shot, reference-less computational wavefront sensing for complex optical fields.Light, science & applications · 2026Article
- Upconversion optical entropy encoding for infrared complex-amplitude imaging.Light, science & applications · 2026Article
- Large zoom ratio and adaptive aberration correction microscope using 4DPSF-aware Physical Degradation-guided Network.Light, science & applications · 2026Article
- Implementation of a Length Gauge Based on Optical Frequency Domain Reflectometry (OFDR).Sensors (Basel, Switzerland) · 2026Article
- Characterization of the 3D-Optical Properties of van der Waals Materials with Deep Learning-Based Coherent Fourier Scatterometry.ACS photonics · 2026Article
- Deep learning and superoscillatory speckles empowered multimode fiber probe for in situ nano-displacement detection and micro-imaging.Nature communications · 2026Article
- Metaoptics merging computational optics and optical computing toward intelligent visual perception.Science advances · 2026Review
- Deep learning-based high dynamic range 3D reconstruction.Scientific reports · 2025Article
- Multi-Task Deep Learning for Surface Metrology.Sensors (Basel, Switzerland) · 2025Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional "physics-based" approach, deep-learning-enabled optical metrology is a kind of "data-driven" approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.