Mr. Haoteng Yin Profile

Haoteng Yin

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Proceedings Article | 13 December 2024 Paper

Single-pixel imaging with TCAO and untrained neural network

Jinyi Deng, Shuang Yu, Wenqing Hong, Xiaojun Li, Haoteng Yin

Proceedings Volume 13501, 135010G (2024) https://doi.org/10.1117/12.3048060

KEYWORDS: Matrices, Modulation, Sampling rates, Image restoration, Neural networks, Feature extraction, Deep learning, Image quality, Sensors, Image processing

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Single-pixel imaging (SPI) is a rapidly evolving computational imaging technique that reconstructs scenes by correlating modulation patterns with measurements captured by a single-pixel detector. Recent advances suggest that integrating model-driven deep learning can significantly enhance the reconstruction quality and robustness of SPI. However, current model-driven SPI methods often rely on ghost imaging (DGI) with random speckles as network input, requiring deeper reconstruction networks to extract effective features, which increases the computational cost. Additionally, random speckles can cause important image details to be obscured by noise at lower sampling rates, making it challenging for the network to produce satisfactory reconstructions. To overcome these limitations, we propose a model-driven SPI method that utilizes an optimized sorting of the Hadamard matrix, termed Total Change Ascending Order (TCAO), as the modulation mask, coupled with an untrained convolutional neural network (CNN) for reconstruction. TCAO is designed to more effectively extract information from scenes at lower sampling rates. The core innovation is integrating deep learning principles across the entire imaging process, assigning more feature extraction tasks to the modulation stage. We refer to this approach as Deep Learning-Based Single-Pixel Imaging with Efficient Sampling (DLES). Simulation results show that DLES allows the network to focus on enhancing reconstruction performance, yielding superior results at lower and even extremely low sampling rates. This method provides a novel approach to simplifying model-driven neural networks while improving the efficiency and quality of single-pixel imaging.

Proceedings Article | 10 May 2023 Paper

A method for dual band infrared image fusion

Lizhen Liu, Canbing Zhao, Yifan Qi, Wenqing Hong, Haoteng Yin, Jie Wang, Yating Li

Proceedings Volume 12554, 125540I (2023) https://doi.org/10.1117/12.2652161

KEYWORDS: Image fusion, Infrared radiation, Infrared imaging, Mid-IR, Long wavelength infrared, Imaging systems, Sensors, Associative arrays, Image processing, Image enhancement

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Middle-wave band infrared imagers and long -wave band infrared imagers have their own advantage and disadvantage under different scenario. Nowadays, the infrared imaging detection system has been developed to the third generation: dual/multi band infrared imager. For a dual/multi band infrared imager, image fusion is a significant research topic. The core of dual band infrared image fusion is to integrate corresponding information from different band infrared imager into a new image. We proposed a new image fusion technology for middle-band infrared image and long-wave infrared image. This technology firstly extracts elementary image semantic, and fuses middle-band infrared image and long-wave infrared image in terms of their elementary image semantic, and then colors the new fusion image in terms of the response characteristics of the scenario in different bands. Finally, the experiment shows the effectiveness of our fusion technology.

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