Zhao Dong

Hebei University of Engineering

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Huaying Wang

Beijing University of Chemical Technology

Huaying Wang

Hebei University of Engineering

Sixing Xi

Hebei University of Engineering

Xianan Jiang

University of California, Los Angeles

Jialiang Lei

Hebei University of Engineering

Jie‐Yu Wang

Southwest University

Xiaolei Zhang

Second Affiliated Hospital of Shantou University Medical College

Yuli Lou

Kunming University of Science and Technology

Wenjian Wang

Shandong Jianzhu University

Lidong He

Nanjing University of Science and Technology

Cooperative Institutions

Hebei University of Engineering

Chinese Academy of Sciences

Northwest University

Beijing Institute of Technology

Beijing Forestry University

Ministry of Education of the People's Republic of China

Peking University

Chongqing University

State Key Laboratory of Continental Dynamics

Nanjing University of Science and Technology

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Resolution enhancement in microscopic imaging based on generative adversarial network with unpaired data

Optics Communications (2021)

Wenjian Wang Huaying Wang Shaokai Yang Xiaolei Zhang Xue Wang

Generative adversarial network

10.1016/j.optcom.2021.127454

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Citations (15)

Ophthalmic space division multiplexing optical coherence tomography (Conference Presentation)

Jason Jerwick Zhao Dong Yongyang Huang Adrienne Saludades Joan DuPont

High speed ophthalmic Optical Coherence Tomography systems are of interest because they allow rapid artifact free widefield retinal imaging. Space Division Multiplexing Optical Coherence Tomography is a high-speed imaging technology which takes advantage of the long coherence length of MEMs VCSEL sources to multiplex multiple images along a single imaging depth. We demonstrate widefield retinal OCT and OCT angiography imaging, acquired at effective Ascan rates of 800,000 Ascans/sec with imaging ranges of up to 11 x 7mm on the retina. A clinical study was conducted to compare ophthalmic SDM-OCT with commercial OCT and OCTa systems.

Superluminescent diode

10.1117/12.2549144

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Citations (0)

A MOEMS accelerometer based on photoelastic effect

Optik (2010)

Dongling Tang Zhao Dong Ying Wang Xiaodong Zhang Zheng Liang

Linearity

Frequency band

Component (thermodynamics)

10.1016/j.ijleo.2010.02.031

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Citations (25)

Phase compensation in digital holographic microscopy using a quantitative evaluation metric

Optics Communications (2018)

Huaying Wang Zhao Dong Xue Wang Yuli Lou Sixing Xi

Digital Holographic Microscopy

Digital holography

Robustness

10.1016/j.optcom.2018.08.061

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Citations (20)

Deep Learning-Based Risk Assessment of High-Voltage Transmission Line Interference with Buried Pipelines

Frontiers in artificial intelligence and applications (2024)

Zhao Dong Liguang Zhang Qiang Wang

In order to solve the influence of high-voltage transmission lines on the service life of buried pipelines, this paper proposes a interference risk assessment method based on deep learning. The IEEE-RTS79 system is used to generate risk data samples, and an indicator system is established from the four dimensions of voltage out of limit, power flow overload, load loss rate and power flow transfer degree to quantify the grid risk, and the data set is labeled. In order to deal with the problem of sample imbalance, a variety of balance methods are used, and dimensions are reduced through feature selection and principal component analysis. Finally, the improved MLP model is used for training. The experimental results show that the computation time of SVM is only 18.3% of that of traditional methods, and the MLP is 1.4%. The computation time is greatly reduced, meeting the real-time requirements. The conclusion is that this method can quickly and accurately assess the power grid risk and effectively improve the service life of buried pipelines.

Line (geometry)

10.3233/faia241198

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Deep learning-based quantitative phase microscopy

Frontiers in Physics (2023)

Wenjian Wang Nauman Ali Ying Ma Zhao Dong Chao Zuo

Quantitative phase microscopy (QPM) is a powerful tool for label-free and noninvasive imaging of transparent specimens. In this paper, we propose a novel QPM approach that utilizes deep learning to reconstruct accurately the phase image of transparent specimens from a defocus bright-field image. A U-net based model is used to learn the mapping relation from the defocus intensity image to the phase distribution of a sample. Both the off-axis hologram and defocused bright-field image are recorded in pair for thousands of virtual samples generated by using a spatial light modulator. After the network is trained with the above data set, the network can fast and accurately reconstruct the phase information through a defocus bright-field intensity image. We envisage that this method will be widely applied in life science and industrial detection.

Sample (material)

Digital Holographic Microscopy

Phase imaging

Spatial light modulator

Light Field

10.3389/fphy.2023.1218147

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Citations (2)

Geometric correction of synchronous scanned Operational Modular Imaging Spectrometer II hyperspectral remote sensing images using spatial positioning data of an inertial navigation system

Journal of Applied Remote Sensing (2015)

Xiaohu Zhou Franz Neubauer Zhao Dong Shichao Xu

The high-precision geometric correction of airborne hyperspectral remote sensing image processing was a hard nut to crack, and conventional methods of remote sensing image processing by selecting ground control points to correct the images are not suitable in the correction process of airborne hyperspectral image. The optical scanning system of an inertial measurement unit combined with differential global positioning system (IMU/DGPS) is introduced to correct the synchronous scanned Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing images. Posture parameters, which were synchronized with the OMIS II, were first obtained from the IMU/DGPS. Second, coordinate conversion and flight attitude parameters’ calculations were conducted. Third, according to the imaging principle of OMIS II, mathematical correction was applied and the corrected image pixels were resampled. Then, better image processing results were achieved.

Imaging spectrometer

Chemical Imaging

Data Processing

10.1117/1.jrs.9.096078

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Citations (1)

Complex amplitude reconstruction based on the spatial carrier phase-shifting method in off-digital holography

Wenjian Wang Huaying Wang Xue Wang Jialiang Lei Jie‐Yu Wang

The complex amplitudes are usually extracted by windowing the Fourier spectra in the off-axis digital holography. However, it often needs manual operation. In order to obtain the complex amplitude automatically from a single off-axis hologram, the spatial carrier frequency phase shifting method is used in this work. Based on the fact that the complex amplitude to retrieve usually vary slowly and the carrier frequency keeps constant, three consecutive phase-shifting interferograms are obtained using the neighboring pixels along the same line in the hologram. Then the complex amplitude is calculated out. Without Fourier transforms, the computational complexity here is much smaller than that of the traditional one. Simulative and experimental results show that this method can really improve the processing efficiency during the complex amplitude reconstruction, and it can be used for automatic application of the digital offaxis holography.

Digital holography

Spatial light modulator

10.1117/12.2577413

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Citations (0)

Cascaded Fresnel digital hologram and its application to watermarking

Optica Applicata (2006)

Shaogeng Deng 刘立人 Lang Haitao Zhao Dong Ximin Liu

A cascaded Fresnel digital hologram (CFDH) is proposed, together with its mathematical derivation. Its application to watermarking has been demonstrated by a simulation procedure, in which the watermark image to be hidden is encoded into the phase of the host image. The watermark image can be deciphered by the CFDH setup, the reconstructed image shows good quality and the error is almost close to zero. Compared with previous technique, this is a lensless architecture which minimizes the hardware requirement, and it is used for the encryption of digital image.

Digital holography

Fresnel integral

Computer-Generated Holography

Source

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Citations (3)

3D positioning and autofocus of the particle field based on the depth-from-defocus method and the deep networks

Machine Learning Science and Technology (2023)

Xiaolei Zhang Zhao Dong Huaying Wang Xiaohui Sha Wenjian Wang

Abstract Accurate three-dimensional positioning of particles is a critical task in microscopic particle research, with one of the main challenges being the measurement of particle depths. In this paper, we propose a method for detecting particle depths from their blurred images using the depth-from-defocus technique and a deep neural network-based object detection framework called you-only-look-once. Our method provides simultaneous lateral position information for the particles and has been tested and evaluated on various samples, including synthetic particles, polystyrene particles, blood cells, and plankton, even in a noise-filled environment. We achieved autofocus for target particles in different depths using generative adversarial networks, obtaining clear-focused images. Our algorithm can process a single multi-target image in 0.008 s, allowing real-time application. Our proposed method provides new opportunities for particle field research.

Autofocus

Particle (ecology)

Position (finance)

10.1088/2632-2153/acdb2e

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Citations (3)