Dr. Weizheng Wang Profile

Dr. Weizheng Wang

at Univ of Changsha Science and Technology

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Publications (3)

Proceedings Article | 12 March 2020 Paper

Performance comparison of coded apertures in push-broom hyperspectral compressed sampling imaging

Mengzhu Li, Weizheng Wang, Junli Qi, Yanfang Guo, Wenjun Yi, Wusheng Tang, Mengjun Zhu, Jiying Liu, Wei Wang, Jubo Zhu, Xujian Li

Proceedings Volume 11438, 114380E (2020) https://doi.org/10.1117/12.2542670

KEYWORDS: Coded apertures, Reconstruction algorithms, Hyperspectral imaging, Coded aperture imaging, Image quality, Imaging spectroscopy

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Proceedings Article | 19 November 2019 Paper

Super-resolution hyperspectral compressed sampling imaging by push-broom coded aperture

Junli Qi, Jiying Liu, Weizheng Wang, Mengzhu Li, Wusheng Tang, Wenjun Yi, Mengjun Zhu, Yanfang Guo, Jubo Zhu, Xiujian Li, Wei Wang

Proceedings Volume 11186, 1118613 (2019) https://doi.org/10.1117/12.2537503

KEYWORDS: Coded apertures, Super resolution, Hyperspectral imaging, Charge-coupled devices, Prisms, Image resolution, Reconstruction algorithms, Coded aperture imaging, Image restoration, Image compression

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Super-resolution hyperspectral imaging is a key technology for many applications, especially in the fields of remote sensing, military, agriculture, and geological exploration. Recovering a high resolution image needs enormous data, which puts forward very high requirements on image system hardware. Compressed sampling spectral imaging technology could well solve this problem and achieve high-resolution objects with low-resolution compressed data. In this paper, the method of a compressed sampling spectral imaging based on push-broom coded aperture and dispersion prism is proposed. A spectral aliasing image is formed when the object passing through the dispersive prism. According to the prism dispersion condition and the CCD pixel size, the visible spectrum can be divided into N spectral bands, and the measurement matrix of the coded aperture is respectively calibrated for the center wavelength of each spectral band. By controlling a stepper to implement the push broom of the coded aperture to change the measurement matrix, multiple spectral aliasing images can be obtained. The pixel size of the coded aperture becomes half of the CCD by a relay lens, which means the pixel of CCD is low-resolution for the coded aperture. The super-resolution hyperspectral image of the object is obtained by the improved LS reconstruction algorithm. Simulation results show that, the recovered hyperspectral image has twice resolution compared with the low-resolution CCD image, and the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) increase with the increasing compressed sampling hyperspectral images. For N=31, the average PSNR and SSIM recovered from six aliasing images is 22.019 and 0.235, respectively. The average PSNR and SSIM of the recovered 31 bands are also increasing with increasing aliasing images. While the aliasing imaging is 156, The average PSNR and SSIM exceeds 38 and 0.9. This method proves that super-resolution hyperspectral imaging can be achieved by capturing less low-resolution object images.

SPIE Journal Paper | 11 April 2019 Open Access

Development of convolutional neural network and its application in image classification: a survey

Wei Wang, Yujing Yang, Xin Wang, Weizheng Wang, Ji Li

OE, Vol. 58, Issue 04, 040901, (April 2019) https://doi.org/10.1117/12.10.1117/1.OE.58.4.040901

KEYWORDS: Convolution, Image classification, Convolutional neural networks, Neural networks, Visualization, Network architectures, Data modeling, Optical engineering, Performance modeling, Neurons

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