Hyperspectral polarization-compressed imaging and reconstruction with sparse basis optimized by particle swarm optimization

Fan, Axin; Xu, Tingfa; Teng, Geer; Wang, Xi; Zhang, Yuhan; Pan, Chenguang

doi:10.1016/j.chemolab.2020.104163

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…In order to compare with feature scaling, we also reconstruct the polarized hyperspectral images not only with the sparse basis optimized by particle swarm optimization (PSO) [ 27 ], but also with non-optimized method. Using the measured image as a reference, the peak signal to noise ratio (PSNR) value of each reconstructed image is calculated for all three kinds methods.…”

Section: Resultsmentioning

confidence: 99%

“…The LCTF serves as a combination of spectral filters to provide the selection for the spectral bands of interest. Moreover, by optimizing the sparse basis with machine learning algorithms, we can successfully reconstruct four Stokes parameters with high accuracy through less than four measurements [ 27 ]. However, the optimization process added to reduce the number of measurements consumes a lot of time, and the optimized sparse basis may not be suitable for some targets that are not involved in optimization.…”

Section: Introductionmentioning

confidence: 99%

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Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging

Fan

Wang

et al. 2020

Sensors

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Polarized hyperspectral images can reflect the rich physicochemical characteristics of targets. Meanwhile, the contained plentiful information also brings great challenges to signal processing. Although compressive sensing theory provides a good idea for image processing, the simplified compression imaging system has difficulty in reconstructing full polarization information. Focused on this problem, we propose a two-step reconstruction method to handle polarization characteristics of different scales progressively. This paper uses a quarter-wave plate and a liquid crystal tunable filter to achieve full polarization compression and hyperspectral imaging. According to their numerical features, the Stokes parameters and their modulation coefficients are simultaneously scaled. The first Stokes parameter is reconstructed in the first step based on compressive sensing. Then, the last three Stokes parameters with similar order of magnitude are reconstructed in the second step based on previous results. The simulation results show that the two-step reconstruction method improves the reconstruction accuracy by 7.6 dB for the parameters that failed to be reconstructed by the non-optimized method, and reduces the reconstruction time by 8.25 h without losing the high accuracy obtained by the current optimization method. This feature scaling method provides a reference for the fast and high-quality reconstruction of physical quantities with obvious numerical differences.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging

Fan

Wang

et al. 2020

Sensors

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“…Recently, compressive full-Stokes polarimeters are constructed with only two commercial components, providing an easy-to-operate and time-saving system. Full-Stokes images can be reconstructed from two measurements compressed by a quarter-wave plate (QWP) and a liquid crystal tunable filter (LCTF) [11][12][13] . Furthermore, benefiting from a retarder followed by a Wollaston prism with a splitting effect, full-Stokes images can be reconstructed from one measurement [14] .…”

Section: Introductionmentioning

confidence: 99%

Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Fan

Teng

et al. 2023

Chin. Opt. Lett.

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Polarized hyperspectral imaging, which has been widely studied worldwide, can obtain four-dimensional data including polarization, spectral, and spatial domains. To simplify data acquisition, compressive sensing theory is utilized in each domain. The polarization information represented by the four Stokes parameters currently requires at least two compressions. This work achieves full-Stokes single compression by introducing deep learning reconstruction. The four Stokes parameters are modulated by a quarter-wave plate (QWP) and a liquid crystal tunable filter (LCTF) and then compressed into a single light intensity detected by a complementary metal oxide semiconductor (CMOS). Data processing involves model training and polarization reconstruction. The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework. Unknown Stokes parameters can be reconstructed from a single compression using the trained model. Benefiting from the acquisition simplicity and reconstruction efficiency, this work well facilitates the development and application of polarized hyperspectral imaging.

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Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China

Bai

Chen²,

Hong

et al. 2023

Geoderma

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Hyperspectral polarization-compressed imaging and reconstruction with sparse basis optimized by particle swarm optimization

Cited by 9 publications

References 28 publications

Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging

Scaling-Based Two-Step Reconstruction in Full Polarization-Compressed Hyperspectral Imaging

Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China

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