Article
Optics
Faliu Yi, Ongee Jeong, Inkyu Moon, Bahram Javidi
Summary: In this study, a deep learning integral imaging system was proposed to reconstruct a 3D image without out of focus areas, enabling target detection and segmentation simultaneously. The Mask-RCNN deep learning algorithm was trained and applied in detecting and segmenting multiple targets in 2D elemental images. The proposed method performed well in the presence of partial occlusions, as demonstrated by experimental results.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Optics
Yuhao Niu, Yubin Liu, Guang Chen, Jieming Zhao, Lin Deng, Ye Sa, Zhan Gao
Summary: In this study, we proposed a new method that allows imaging of multiple objects with strong background illumination. By isolating the speckle pattern of each object through image difference process and eliminating background noise interference with principal components analysis (PCA) technique, our method can provide better observations of the objects in bright field scenarios.
OPTICS AND LASER TECHNOLOGY
(2024)
Article
Chemistry, Analytical
Gilsu Yeo, Myungjin Cho
Summary: This paper proposes a new method for three-dimensional visualization of objects at long distance under photon-starved conditions. The method utilizes digital zooming to improve visual quality by cropping and interpolating the region of interest from the image. Photon counting integral imaging is used to reconstruct three-dimensional images, and multiple observation photon counting integral imaging is employed for more accurate estimation. Optical experiments and performance metrics calculation demonstrate the feasibility and effectiveness of the proposed method in improving the visualization of three-dimensional objects at long distances under photon-starved conditions.
Article
Chemistry, Analytical
Seung-Chan Lim, Myungjin Cho
Summary: An efficient wireless 3D image transmission system is proposed based on the MIMO technique to reconstruct high-resolution 3D digital content in integral imaging. Multiple elemental images are transmitted simultaneously and recovered with a linear receiver using the spatial multiplexing capability. The proposed system achieves excellent 3D reconstruction performance in terms of visual quality and peak sidelobe ratio.
Article
Multidisciplinary Sciences
Flavien Bureau, Justine Robin, Arthur Le Ber, William Lambert, Mathias Fink, Alexandre Aubry
Summary: Matrix imaging is revolutionizing wave physics by optimizing compensation for aberrations and multiple scattering in heterogeneous media. This paper extends ultrasound matrix imaging to a 3D geometry and proposes a non-invasive approach to make the skull transparent for improved brain imaging.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Analytical
Jiheon Lee, Myungjin Cho
Summary: This paper proposes an enhancement of three-dimensional image visualization techniques by using different pickup plane reconstructions to improve both the lateral and longitudinal resolutions of 3D objects.
Article
Engineering, Electrical & Electronic
Moein Nazari, Rouzbeh Moini, Simon Fortin, Farid P. Dawalibi
Summary: Accurate solutions of electromagnetic scattering problems involving materials with large permittivity contrasts are explored using different commonly used combined surface integral equations (SIEs). The accuracy of the results and potential numerical instabilities are investigated in detail for different frequencies and permittivity values. The study specifically focuses on the analysis of a dielectric resonator (DR) with cubic geometry, which requires higher contrast materials to achieve smaller DR type antennas. The accuracy of the combined surface integral equations in determining the natural resonant modes of the DR is examined, emphasizing the need for a proper combination of electric and magnetic fields equations to accurately determine the resonance modes by exploring the radar cross section (RCS) of the DR in free space.
IET MICROWAVES ANTENNAS & PROPAGATION
(2023)
Article
Environmental Sciences
Xi Wang, Tingfa Xu, Yuhan Zhang, Axin Fan, Chang Xu, Jianan Li
Summary: This paper proposes a method to reconstruct compressed hyperspectral (HS) data using convolutional neural networks (CNNs). By dividing the imaging process into multiple steps and building a subnetwork for each step, this method can reconstruct compressed HS data quickly and accurately, while having superior resistance to noise.
Review
Cell Biology
Ruslan Dmitriev, Xavier Intes, Margarida M. Barroso
Summary: Luminescence lifetime imaging offers a unique window into the physiological and structural environment of cells and tissues, enabling a new level of functional and molecular analysis. It provides rich information on cell metabolism, protein-protein interaction networks, and other physiological parameters at multiple scales. The technology allows for 3D spatially resolved and longitudinal measurements ranging from microscopic to macroscopic scale.
JOURNAL OF CELL SCIENCE
(2021)
Article
Optics
Li-Xing Lin, Jie Cao, Dong Zhou, Qun Hao
Summary: The illumination patterns of a ghost imaging system are disturbed when passing through a scattering medium. Within limits, larger speckle size provides stronger anti-interference ability to scattering medium, but leads to lower imaging resolution. Computational ghost imaging system allows flexible design of illumination patterns. We analyze and demonstrate that using random superimposed speckle patterns as the illumination pattern enhances the anti-interference ability without reducing imaging resolution when using intensity fluctuation correlation reconstruction algorithm. Experimental results show significant improvement in contrast-to-noise ratio (CNR) and imaging resolution.
OPTICS COMMUNICATIONS
(2023)
Article
Engineering, Mechanical
Nikita Letov, Pavan Tejaswi Velivela, Siyuan Sun, Yaoyao Fiona Zhao
Summary: Geometric solid modeling is crucial for engineering design purposes and has been used in CAD software for transferring geometric information to manufacturers. With the emergence of additive manufacturing, CAD files can now be directly used for production, including complex geometric objects inspired by nature. However, modeling such complex structures still faces challenges, highlighting the need for novel geometric modeling methods to support bio-inspired design.
JOURNAL OF MECHANICAL DESIGN
(2021)
Article
Optics
Enlai Guo, Yingjie Shi, Lianfa Bai, Jing Han
Summary: By analyzing the physical prior of speckle image redundancy, this paper constructs a new neural network named AESINet to recover complex targets hidden behind opaque medium using adaptive encoding. This method successfully reduces the redundancy of speckle, improves the separability of data, and enhances the quality of target recovery.
Article
Optics
Kashif Usmani, Gokul Krishnan, Timothy O'Connor, Bahram Javidi
Summary: Polarimetric imaging is effective for object recognition and material classification by capturing polarimetric signatures of objects. A novel approach using deep learning for 3D polarimetric integral imaging in degraded environments such as low light and occlusions outperforms traditional imaging methods.
Article
Materials Science, Multidisciplinary
Hiroko Yokota, Takeshi Hayashida, Dan Kitahara, Tsuyoshi Kimura
Summary: This study proposes the use of CID-SHG technique to investigate ferroaxial order and its domain states and successfully visualizes three-dimensional images of ferroaxial domain structures in NiTiO3. The results indicate that CID-SHG is a sensitive probe and opens possibilities for the use of ferroaxial materials in nonlinear optical manipulations.
NPJ QUANTUM MATERIALS
(2022)
Article
Chemistry, Analytical
Hyun-Woo Kim, Myungjin Cho, Min-Chul Lee
Summary: This study focuses on the research of three-dimensional reconstruction under low illumination environment. Photon-counting integral imaging is a technique to visualize 3D images under low light conditions. However, the conventional method has the issue of randomness due to the temporal and spatial independence of Poisson random numbers. In this paper, the Kalman filter is applied to improve the visual quality by correcting the errors in the photon-counting integral imaging. The proposed method shows better structure similarity, peak signal-to-noise ratio, and cross-correlation values compared to the conventional method, indicating a more accurate visualization of low illuminated images. Furthermore, it is expected to contribute to the development of autonomous driving technology and security camera technology.
