A spectral polarization camera based on ghost imagingvia sparsity constraints

2021 ◽  
Author(s):  
chunyan chu ◽  
Shengying Liu ◽  
Zhentao Liu ◽  
Chenyu Hu ◽  
Yuejin Zhao ◽  
...  
Keyword(s):  
Sparsity Constraints ◽  
Polarization Camera

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

scholarly journals The Balloon-Borne Investigation of Temperature and Speed of Electrons in the Corona (BITSE): Mission Description and Preliminary Results

Solar Physics ◽  
2021 ◽  
Vol 296 (1) ◽  
Author(s):  
N. Gopalswamy ◽  
J. Newmark ◽  
S. Yashiro ◽  
P. Mäkelä ◽  
N. Reginald ◽  
...  
Keyword(s):  
Heliocentric Distance ◽  
Signal To Noise Ratio ◽  
Flow Speed ◽  
Science Data ◽  
Uncertainty Interval ◽  
Heliospheric Observatory ◽  
Flash Storage ◽  
Goddard Space Flight Center ◽  
Good Agreement ◽  
Polarization Camera

AbstractWe report on the Balloon-borne Investigation of Temperature and Speed of Electrons in the corona (BITSE) mission launched recently to observe the solar corona from $\approx 3$ ≈ 3  Rs to 15 Rs at four wavelengths (393.5, 405.0, 398.7, and 423.4 nm). The BITSE instrument is an externally occulted single stage coronagraph developed at NASA’s Goddard Space Flight Center in collaboration with the Korea Astronomy and Space Science Institute (KASI). BITSE used a polarization camera that provided polarization and total brightness images of size $1024 \times 1024$ 1024 × 1024 pixels. The Wallops Arc Second Pointer (WASP) system developed at NASA’s Wallops Flight Facility (WFF) was used for Sun pointing. The coronagraph and WASP were mounted on a gondola provided by WFF and launched from the Fort Sumner, New Mexico station of Columbia Scientific Balloon Facility (CSBF) on September 18, 2019. BITSE obtained 17,060 coronal images at a float altitude of $\approx \mbox{128,000}$ ≈ 128,000 feet ($\approx 39$ ≈ 39  km) over a period of $\approx 4$ ≈ 4  hrs. BITSE flight software was based on NASA’s core Flight System, which was designed to help develop flight quality software. We used EVTM (Ethernet Via Telemetry) to download science data during operations; all images were stored on board using flash storage. At the end of the mission, all data were recovered and analyzed. Preliminary analysis shows that BITSE imaged the solar minimum corona with the equatorial streamers on the east and west limbs. The narrow streamers observed by BITSE are in good agreement with the geometric properties obtained by the Solar and Heliospheric Observatory (SOHO) coronagraphs in the overlapping physical domain. In spite of the small signal-to-noise ratio ($\approx 14$ ≈ 14 ) we were able to obtain the temperature and flow speed of the western steamer. In the heliocentric distance range 4 – 7 Rs on the western streamer, we obtained a temperature of $\approx 1.0\pm 0.3$ ≈ 1.0 ± 0.3  MK and a flow speed of $\approx 260$ ≈ 260  km s−1 with a large uncertainty interval.

Sub-Rayleigh ghost imaging via sparsity constraints based on a digital micro-mirror device

2013 ◽  
Vol 377 (31-33) ◽  
pp. 1844-1847 ◽  
Author(s):  
Jie Chen ◽  
Wenlin Gong ◽  
Shensheng Han
Keyword(s):  
Ghost Imaging ◽  
Sparsity Constraints

A Lensless Polarization Camera

2021 ◽  
Author(s):  
Shay Elmalem ◽  
Raja Giryes
Keyword(s):  
Polarization Camera

scholarly journals Graph diffusion distance: Properties and efficient computation

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249624
Author(s):  
C. B. Scott ◽  
Eric Mjolsness
Keyword(s):  
Distance Measure ◽  
Fixed Time ◽  
Graph Laplacian ◽  
Upper Bounds ◽  
Distance Measures ◽  
Distance Metrics ◽  
Graph Products ◽  
Efficient Computation ◽  
New Family ◽  
Sparsity Constraints

We define a new family of similarity and distance measures on graphs, and explore their theoretical properties in comparison to conventional distance metrics. These measures are defined by the solution(s) to an optimization problem which attempts find a map minimizing the discrepancy between two graph Laplacian exponential matrices, under norm-preserving and sparsity constraints. Variants of the distance metric are introduced to consider such optimized maps under sparsity constraints as well as fixed time-scaling between the two Laplacians. The objective function of this optimization is multimodal and has discontinuous slope, and is hence difficult for univariate optimizers to solve. We demonstrate a novel procedure for efficiently calculating these optima for two of our distance measure variants. We present numerical experiments demonstrating that (a) upper bounds of our distance metrics can be used to distinguish between lineages of related graphs; (b) our procedure is faster at finding the required optima, by as much as a factor of 103; and (c) the upper bounds satisfy the triangle inequality exactly under some assumptions and approximately under others. We also derive an upper bound for the distance between two graph products, in terms of the distance between the two pairs of factors. Additionally, we present several possible applications, including the construction of infinite “graph limits” by means of Cauchy sequences of graphs related to one another by our distance measure.

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