Mr. Yi Chen Profile

Yi Chen

at National Univ. of Defense Technology

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Publications (11)

Proceedings Article | 1 February 2024 Paper

Research on low-light image enhancement algorithm based on filtering and data-driven composite

Jiang Wu, Zhengdong Cheng, Yi Chen, Bo Xie

Proceedings Volume 13068, 1306822 (2024) https://doi.org/10.1117/12.3016282

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Proceedings Article | 4 April 2023 Paper

Infrared small target detection algorithm based on hetero-range feature fusion

Bin Shao, Hua Yang, Bin Zhu, Yi Chen, Rongping Zou

Proceedings Volume 12617, 1261715 (2023) https://doi.org/10.1117/12.2664216

KEYWORDS: Infrared radiation, Target detection, Infrared imaging, Infrared detectors, Detection and tracking algorithms, Image segmentation, Feature extraction, Transformers, Convolution, Image fusion

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The current convolution-based semantic segmentation network lacks long-range dependencies for infrared small target detection, which may lead to unsatisfactory detection results in the real scenario. To address the problem, this paper proposed a semantic segmentation network based on hetero-range feature fusion (HRFFnet). Compared with the common semantic segmentation networks, this network includes two feature extraction branches. One is a short-range extraction branch consisting of convolution operations, and the other is a long-range feature extraction branch consisting of transformer. The HRFFnet complements the local features extracted by the convolutional neural network by adding the transformer structure to the segmentation network to introduce the long-range information of image. And this paper also designed a hetero-range fusion module. This module is based on atrous spatial pyramid pooling and adds shortcut connection to fuse different ranges of features extracted from images, which can maintain resolution of image and improve ability of feature representation. The hetero-range fusion module fuses long-range dependencies and short-range information extracted by transformer and convolution to capture multi-scale contextual information about the scene in infrared images and facilitate the interchange of information between different features. To evaluate the HRFFnet, we compare the performance of our network against other high-performance convolution-based methods and transformer-based networks on the open SIRST dataset with different evaluation metrics. The proposed method achieves a better combined results in terms of intersection of Dice coefficient, pixel accuracy, intersection over union and receiver operating characteristic curve. The experiments and results show that the network has some superiority: one is that it can break through the limitation of range of extracting features when only using convolutional network or transformer-based network; the other one is that this network can perform better with good robustness against real and complex scenarios. So, the proposed algorithm has broad application prospects in border patrol and urban security fields.

Proceedings Article | 4 April 2023 Paper

Heterogeneous remote sensing image matching algorithm based on hierarchical feature balanced sampling convolution cross-correlation framework

Rongping Zou, Bin Zhu, Yi Chen

Proceedings Volume 12617, 126170X (2023) https://doi.org/10.1117/12.2663738

KEYWORDS: Remote sensing, Synthetic aperture radar, Data fusion, Neural networks

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Synthetic Aperture Radar (SAR) imaging and visible light imaging are the two most commonly used imaging methods for remote sensing satellites. Since the imaging information of the two is highly complementary, many scenarios of data fusion need to use these two heterogeneous data. However, before data fusion, the data of the two modalities need to be aligned, and the performance of heterogeneous data matching algorithm directly affects the performance of obtaining ground control points during alignment. At present, there are many one-stage and two-stage methods for heterogeneous remote sensing image matching. The existing one-stage methods have problems such as large average prediction offset, low matching accuracy, and imbalance of features at different levels when combining features. The stage method cannot meet the actual needs in terms of speed and accuracy. To address these issues, this paper proposes an end-to-end heterogeneous remote sensing image matching framework HB3CF. The framework uses the classic image feature extraction network to construct a pair of pseudo-twin networks, which extract features from two heterogeneous images respectively. Then, the features of each level are uniformly sampled on the channel using the convolution layer to reduce the weight of the high-dimensional features in the joint features, which effectively improves the expression ability of the features of each level of the model. Finally, the matching results are obtained by performing convolution, cross-correlation and up-sampling operations on the high-dimensional features of the SAR image and the optical image. Experiments show that the average offset error of the model is reduced by about 25% compared to state-of-the-art methods. When the average offset error is less than or equal to 0 pixel, 1 pixel, 2 pixel and 3 pixel, the accuracy is increased by 8.53%, 9.54%, 4.16% and 1.12% respectively, reaching 25.90%, 65.03%, 86.65% and 92.15%, which greatly improves the accuracy of the matching method of heterogeneous images, and explores the application of deep learning methods in large-scale heterogeneous remote sensing image matching tasks.

Proceedings Article | 6 March 2023 Paper

Rotational random extraction of Toeplitz matrix for ghost imaging

Zhengdong Cheng, Maosheng Sun, Yi Chen, Haikuan Chang, Yanyan Liu

Proceedings Volume 12553, 1255313 (2023) https://doi.org/10.1117/12.2667775

KEYWORDS: Modulation, Matrices, Image quality, Sensors, Image restoration, Detection and tracking algorithms, Computer simulations, Image resolution, Angular velocity, Sampling rates

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Proceedings Article | 8 February 2019 Paper

Effects of wavelength on polarization correlated imaging of rough surface objects

Xiang Zhai, Fan Xiang, Zhengdong Cheng, Yi Chen, Zhenyu Liang

Proceedings Volume 10843, 108430D (2019) https://doi.org/10.1117/12.2505119

KEYWORDS: Polarization, Refractive index, Scattering, Aluminum, Bidirectional reflectance transmission function, Beam splitters, Light scattering, Spectral models, Sensors, Picosecond phenomena

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Proceedings Article | 30 January 2019 Paper

Analysis of influence of mechanical vibration on ghost imaging

Yi Chen, Zheng-dong Cheng , Xiang Fan , Yu-bao Cheng , Bin Zhu

Proceedings Volume 10841, 108410U (2019) https://doi.org/10.1117/12.2507522

KEYWORDS: Modulation transfer functions, Imaging systems, Digital micromirror devices, Modulation, Compressed sensing, Diffusion, Target detection

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SPIE Journal Paper | 30 January 2019

Compressive sensing ghost imaging object detection using generative adversarial networks

Xiang Zhai, Zhengdong Cheng, Yuan Wei, Zhenyu Liang, Yi Chen

OE, Vol. 58, Issue 01, 013108, (January 2019) https://doi.org/10.1117/12.10.1117/1.OE.58.1.013108

KEYWORDS: Compressed sensing, Gallium nitride, Image resolution, Reconstruction algorithms, Data modeling, Image processing, Matrices, Speckle pattern, Digital micromirror devices, Detection and tracking algorithms

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Proceedings Article | 5 November 2018 Paper

Adaptive polarization compressive ghost imaging based on principal component analysis

Xiang Zhai, Xiang Fan, Zhengdong Cheng, Yi Chen, Zhenyu Liang

Proceedings Volume 10816, 108161A (2018) https://doi.org/10.1117/12.2500888

KEYWORDS: Polarization, Principal component analysis, Digital micromirror devices, Linear polarizers, Detection and tracking algorithms

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SPIE Journal Paper | 28 July 2018

Ghost imaging with N-order speckle light

Yi Chen, Zhengdong Cheng, Yubao Cheng, Bin Zhu, Xiang Zhai

OE, Vol. 57, Issue 07, 073111, (July 2018) https://doi.org/10.1117/12.10.1117/1.OE.57.7.073111

KEYWORDS: Speckle pattern, Speckle, Digital micromirror devices, Speckle imaging, Imaging systems, Sensors, Modulation, Light sources, Image quality, Reconstruction algorithms

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Proceedings Article | 10 February 2017 Paper

The influence of intensity correlation order on lensless ghost imaging

Zhenyu Liang, Xiang Fan, Zhengdong Cheng, Bin Zhu, Yi Chen

Proceedings Volume 10250, 102500J (2017) https://doi.org/10.1117/12.2266640

KEYWORDS: Image quality

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Proceedings Article | 7 November 2016 Paper

Application of joint orthogonal bases in compressive sensing ghost image

Xiang Fan, Yi Chen, Zheng-dong Cheng, Zheng-yu Liang, Bin Zhu

Proceedings Volume 10141, 101410Q (2016) https://doi.org/10.1117/12.2253814

KEYWORDS: Compressed sensing, Fourier transforms, Image quality, Joint transforms, Light, Discrete wavelet transforms, Reconstruction algorithms, Imaging systems, Sensors, Time metrology

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Showing 5 of 11 publications

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