Shape-from-focus for real-time terahertz 3D imaging

Perraud, Jean-Baptiste; Guillet, Jean-Paul; Redon, O.; Hamdi, Maher; Simoens, F.; Mounaix, Patrick

doi:10.1364/ol.44.000483

Cited by 31 publications

(13 citation statements)

References 10 publications

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“…Apart from well-established THz tomography technique, intelligent ideas were taken from the optical range to cope with obstacles in different imaging scenarios. Recent achievements for multi-thin layers 3D scheme reconstruction witness this by the works of 'Shape-fromfocus for real-time terahertz 3D imaging' [264], 'Multi-layered full-field phase imaging using continuous-wave terahertz ptychography' [265], and 'Coherent reconstruction of a textile and a hidden object with terahertz radiation' [258]. All of the three methods were developed by using the cameras working at THz range to scan in space.…”

Section: Thz Computational Imagingmentioning

confidence: 99%

Roadmap of Terahertz Imaging 2021

Valušis

Lisauskas

Yuan

et al. 2021

Sensors

157

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In this roadmap article, we have focused on the most recent advances in terahertz (THz) imaging with particular attention paid to the optimization and miniaturization of the THz imaging systems. Such systems entail enhanced functionality, reduced power consumption, and increased convenience, thus being geared toward the implementation of THz imaging systems in real operational conditions. The article will touch upon the advanced solid-state-based THz imaging systems, including room temperature THz sensors and arrays, as well as their on-chip integration with diffractive THz optical components. We will cover the current-state of compact room temperature THz emission sources, both optolectronic and electrically driven; particular emphasis is attributed to the beam-forming role in THz imaging, THz holography and spatial filtering, THz nano-imaging, and computational imaging. A number of advanced THz techniques, such as light-field THz imaging, homodyne spectroscopy, and phase sensitive spectrometry, THz modulated continuous wave imaging, room temperature THz frequency combs, and passive THz imaging, as well as the use of artificial intelligence in THz data processing and optics development, will be reviewed. This roadmap presents a structured snapshot of current advances in THz imaging as of 2021 and provides an opinion on contemporary scientific and technological challenges in this field, as well as extrapolations of possible further evolution in THz imaging.

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Section: Thz Computational Imagingmentioning

confidence: 99%

Roadmap of Terahertz Imaging 2021

Valušis

Lisauskas

Yuan

et al. 2021

Sensors

157

View full text Add to dashboard Cite

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“…If the filter is applied as pre-FM application then before recovering the depth map, FM is applied onP i,j (k) to obtain i,j (k) using (3). However, if the filter application is post-FM, then the depth map can be recovered directly usingˆ i,j (k).…”

Section: B Shape Recoverymentioning

confidence: 99%

Sampling-Noise Modeling & Removal in Shape From Focus Systems Through Kalman Filter

et al. 2021

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Shape from Focus (SFF) is one of the passive techniques to recover the shape of an object under consideration. It utilizes the focus cue present in the stack of images, obtained by a single camera. In SFF when the images are acquired, the inter-frame distance, also known as the sampling step size, is assumed to be constant. However, in practice, due to mechanical constraints, sampling step size cannot remain constant. The inconsistency in the sampling step size causes the problem of jitter, and produces Jitter noise in focus curves. This Jitter noise is not visible in images, because each pixel in an image (of the stack) will be subjected to the same error in focus. Thus, traditional image denoising techniques will not work. This paper formulates a model of the Jitter noise, followed by the design of system and measurement models for Kalman filter. Then, the jittering problem for SFF systems is solved using the proposed filtering technique. Experiments are performed on simulated and real objects. Ten noise levels are considered for simulated, and four for real objects. RMSE and Correlation are used to measure the reconstructed shape. The results show the effectiveness of the proposed scheme.

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“…The final 3D shape can be accurately recovered by applying surface interpolation methods on this 3D point cloud. Applications of SFF can be found in various fields, like mobile robotics, surface topography, macroscopic/microscopic analysis, depth approximation, and medical diagnostics (Shiqi and Seita (2019); Tang, Qiu, and Li (2019); Ahmad and Choi (2007); Perraud et al (2019); Wendland (2018)).…”

Section: Introductionmentioning

confidence: 99%

A simplified approach using deep neural network for fast and accurate shape from focus

Mutahira

Muhammad

et al. 2020

Microscopy Res & Technique

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Three-dimensional shape recovery is an important issue in the field of computer vision. Shape from Focus (SFF) is one of the passive techniques that uses focus information to estimate the three-dimensional shape of an object in the scene. Images are taken at multiple positions along the optical axis of the imaging device and are stored in a stack. In order to reconstruct the three dimensional shape of the object, the best-focused positions are acquired by maximizing the focus curves obtained via application of a focus measure operator. In this article, Deep Neural Network (DNN) is employed to extract the more accurate depth of each object point in the image stack. The size of each image in the stack is first reduced and then provided to the proposed DNN network to aggregate the shape. The initial shape is refined by applying a median filter, and later the reconstructed shape is sized back to original by utilizing bi-linear interpolation. The results are compared with commonly used focus measure operators by employing root mean squared error (RMSE), correlation, and image quality index (Q). Compared to other methods, the proposed SFF method using DNN shows higher precision and low computational time consumption.

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Shape-from-focus for real-time terahertz 3D imaging

Cited by 31 publications

References 10 publications

Roadmap of Terahertz Imaging 2021

Roadmap of Terahertz Imaging 2021

Sampling-Noise Modeling & Removal in Shape From Focus Systems Through Kalman Filter

A simplified approach using deep neural network for fast and accurate shape from focus

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