Experimental Results of the Balloon-Borne Spectral Camera Based on Ghost Imaging via Sparsity Constraints

Wu, Jiang; Li, Enrong; Shen, Xia; Yao, Shiyi; Tong, Zhishen; Hu, Chenyu; Liu, Zhentao; Liu, Shengying; Tan, Shiyu; Han, Shensheng

doi:10.1109/access.2018.2879849

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…Compared to imaging systems with array sensors, SPI also possesses advantages such as high signal-to-noise ratios and wide spectral ranges. It has been applied to various applications, including remote sensing [4,5], threedimensional imaging [6,7], terahertz imaging [8], privacy protection [9], rapid localization of moving objects [10], and multispectral imaging [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Super-Resolution Networks for Single-Pixel Imaging at Ultra-Low Sampling Rates

Liu,

Zhang,

Zhou

et al. 2024

IEEE Access

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Single-pixel imaging (SPI) leverages sequential pattern illumination and intensity detection to reconstruct images, facing the challenge of balancing high-resolution output with ultra-low sampling rates for rapid imaging processes. We introduce a network architecture specifically tailored for SPI, which demonstrates improved performance even before integrating with SPI's physical sampling processes. This integration, particularly focusing on the nuanced effects of sampling rates within the model's loss function and data preprocessing, enhances image reconstruction quality and adaptability at low sampling rates, down to 1.56%. Our approach achieves a balance between advanced computational methods and the physical principles of SPI, resulting in a peak signal-to-noise ratio of 30.93 dB, a structural similarity index measure of 0.8818, and a perceptual index (PI) of 5.31 at a 6.25% sampling rate, alongside a notable PI of 2.68 at a 1.56% sampling rate in practical tests. By merging sophisticated network design with strategic integration of physical sampling rates, our model provides a refined solution for high-quality, high-resolution SPI at minimal sampling rates, facilitating progress in ultra-fast imaging applications.

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Section: Introductionmentioning

confidence: 99%

Adaptive Super-Resolution Networks for Single-Pixel Imaging at Ultra-Low Sampling Rates

Liu,

Zhang,

Zhou

et al. 2024

IEEE Access

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“…The spectral camera incorporates compressed sensing theory [5] to perform signal acquisition at a frequency lower than the Nyquist frequency, thus improving the efficiency of optical channel capacity utilization and achieving information compression in the imaging acquisition process. Jianrong Wu et al [6] demonstrated the feasibility of using a GISC spectral camera for Earth observation applications by utilizing a tethered balloon loaded with the GISC spectral camera prototype in 2018. However, the engineering application of the GISC spectral camera is still distant, as the imaging quality of the GISC spectral camera is imperative in its practical application.…”

Section: Introductionmentioning

confidence: 99%

Improved U-Net3+ With Spatial–Spectral Transformer for Multispectral Image Reconstruction

Chen

Li³

et al. 2023

IEEE Photonics J.

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Multispectral image reconstruction, which aims to recover a three-dimensional (3D) spatial-spectral signal from a two-dimensional measurement in a spectral camera based on ghost imaging via sparsity constraint (GISC), has been attracting much attention recently. However, faced with abundant 3D spectral data, the reconstruction quality cannot meet the visual requirements. Based on the robust data processing capability of deep learning, a novel network called SSTU-Net3+ is constructed by improving U-Net3+ with a spatial-spectral transformer (SST). To enhance the feature representation of images during reconstruction, mixed pooling modules and new convolution processes are proposed to improve the performance of the encoder and decoder, with U-Net3+ as the backbone. To boost the quality of reconstructed images, with split and concatenate (Concat) operations, we construct SST modules by exploiting both spatial and spectral correlations of multispectral images to refine the spatial and spectral features. Furthermore, we employ the SST in the decoder to reconstruct the desired 3D cube. Given similar network parameters, experiments on GISC spectral imaging data show that, compared to convolutional neural network-based methods, the average peak signal-to-noise ratio of images reconstructed using SSTU-Net3+ is improved by 3%, the structural similarity is enhanced by 3%, and the spectral angle mapping is cut by 12%. Particularly, compared to differential ghost imaging and compressed sensing, the reconstruction quality of SSTU-Net3+ has been significantly improved. SSTU-Net3+ can process a large amount of 3D multispectral image data more efficiently and construct the target image more accurately than the abovementioned methods. 

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“…In 2014, the research group of Han Shensheng of the Shanghai Institute of Optics and Mechanics [14] proposed a snapshot-type sparsely constrained quantum correlation imaging (GISC) spectral imaging system, and verified the feasibility of the imaging scheme in principle through compression imaging experiments. In 2018, the research group [15] used an 800-mhigh tethered balloon to carry a snapshot-type GISC spectroscopic camera to achieve snapshot-type passive optical multispectral quantum correlation imaging of natural scenes. In the same year, the research group [16] proposed adding a flat-field grating to the snapshot GISC spectroscopic camera, which can increase the spectral resolution to 1 nm and realize the imaging function of the GISC hyperspectral camera, but the system structure is complex, which is not conducive to engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Research on target recognition technology of GISC spectral imaging based on active laser lighting

Wang

2022

Front. Phys.

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Aiming at the application requirements of spectral imaging technology in satellite remote sensing, biomedical diagnosis, marine detection and rescue, agricultural and forestry monitoring and classification, military camouflage identification, etc., this paper uses 532 and 650 nm lasers as light sources, and uses multi-spectral intensity correlation imaging equipment—snapshot spectroscopic cameras based on ghost imaging via sparsity constraints (GISC) enable precise identification of targets. In this paper, the principle of snapshot GISC spectral imaging is expounded, and the experimental research work of GISC spectral imaging target recognition technology based on active laser illumination is carried out. The experimental results show that using a 532 nm laser as the light source to illuminate the target object can accurately identify the green target letter “I”; using a 650 nm laser as the light source to illuminate the target object can accurately identify the red target letter “Q”. And gives spectral imaging results of the color target “QIT” acquired by the GISC spectroscopic camera through a single exposure at the wavelength range from 446 to 698nm, with both pseudo-color map and color fusion map. In order to further illustrate the feasibility of the experiment, the spectral distribution of the reconstructed image is analyzed, which has important practical significance and engineering value.

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Experimental Results of the Balloon-Borne Spectral Camera Based on Ghost Imaging via Sparsity Constraints

Cited by 10 publications

References 22 publications

Adaptive Super-Resolution Networks for Single-Pixel Imaging at Ultra-Low Sampling Rates

Adaptive Super-Resolution Networks for Single-Pixel Imaging at Ultra-Low Sampling Rates

Improved U-Net3+ With Spatial–Spectral Transformer for Multispectral Image Reconstruction

Research on target recognition technology of GISC spectral imaging based on active laser lighting

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