Abstract:We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pa… Show more
“…13f–i ). Enhancement : Shi et al 51 proposed a fringe-enhancement method based on deep learning, and the flowchart of which is given in Fig. 14a .…”
Section: The Use Of Deep Learning In Optical Metrologymentioning
confidence: 99%
“…a – d Adapted with permission from ref. 51 , Optica Publishing Fig. 15 Flowchart of the single-frame phase retrieval approach using deep learning and the 3D reconstruction results of different approaches.…”
Section: The Use Of Deep Learning In Optical Metrologymentioning
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.
“…13f–i ). Enhancement : Shi et al 51 proposed a fringe-enhancement method based on deep learning, and the flowchart of which is given in Fig. 14a .…”
Section: The Use Of Deep Learning In Optical Metrologymentioning
confidence: 99%
“…a – d Adapted with permission from ref. 51 , Optica Publishing Fig. 15 Flowchart of the single-frame phase retrieval approach using deep learning and the 3D reconstruction results of different approaches.…”
Section: The Use Of Deep Learning In Optical Metrologymentioning
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.
“…The neural networks used include the fully convolutional network (FCN), AEN and UNet. Jiashuo Shi et al [ 65 ] took advantage of a deep neural network (DNN) in the phase expansion of fringe projections and used the DnCNN model to learn and train the fringe extraction process, which enhanced the phase recovery of a single-frame fringe image. Jiaming Qian et al [ 66 ] developed a technology that can realize all-round 360° 3D reconstruction based on fringe projection profilometry and relieved the limitation of viewing the angle occlusion of traditional equipment.…”
The three-dimensional (3D) size and morphology of high-temperature metal components need to be measured in real time during manufacturing processes, such as forging and rolling. Since the surface temperature of a metal component is very high during the forming and manufacturing process, manually measuring the size of a metal component at a close distance is difficult; hence, a non-contact measurement technology is required to complete the measurement. Recently, machine vision technology has been developed, which is a non-contact measurement technology that only needs to capture multiple images of a measured object to obtain the 3D size and morphology information, and this technology can be used in some extreme conditions. Machine vision technology has been widely used in industrial, agricultural, military and other fields, especially fields involving various high-temperature metal components. This paper provides a comprehensive review of the application of machine vision technology in measuring the 3D size and morphology of high-temperature metal components. Furthermore, according to the principle and method of measuring equipment structures, this review highlights two aspects in detail: laser scanning measurement and multi-view stereo vision technology. Special attention is paid to each method through comparisons and analyses to provide essential technical references for subsequent researchers.
“…For instance, an autoencoder-based network named UNet can serve as an end-to-end network to acquire the depth map from a single structured-light image [28][29][30][31]. Works presented in [32][33][34][35][36] reveal that a phase map can be retrieved by one or multiple neural networks from structured-light images, and the phase map is then used to calculate the depth map.…”
Accurate three-dimensional (3D) shape reconstruction of objects from a single image is a challenging task, yet it is highly demanded by numerous applications. This paper presents a novel 3D shape reconstruction technique integrating a high-accuracy structured-light method with a deep neural network learning scheme. The proposed approach employs a convolutional neural network (CNN) to transform a color structured-light fringe image into multiple triple-frequency phase-shifted grayscale fringe images, from which the 3D shape can be accurately reconstructed. The robustness of the proposed technique is verified, and it can be a promising 3D imaging tool in future scientific and industrial applications.
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