Determination of the Spatial Resolution in the Case of Imaging Magnetic Fields by Polarized Neutrons

Treimer, W.; Köhler, Ralf

doi:10.3390/app11156973

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Zhang et al [7] improved the reconstruction algorithm through sum difference reconstruction, but there is still an urgent need for a fully polarized imaging system with high resolution in practical application. Due to the influence of imaging distance and atmospheric disturbance, the limit resolution of the image projected on the focal plane is greatly reduced (much smaller than the optical system diffraction limit resolution [10]), resulting in a lower spatial resolution of the polarized image. Moreover, the spatial resolution of the image is limited by the number of detector pixels.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale adaptive weighted network for polarization computational imaging super-resolution

Wang

Zhang

et al. 2022

Appl. Phys. B

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Due to the pixel limit of the polarization imaging detector and the object detection conditions, the spatial resolution of the object polarization image in actual imaging detection applications is generally low. Convolutional neural networks (CNNs) have been introduced to image super-resolution (SR). However, these methods rarely explore the internal connections between local spatial components and multi-scale feature maps in different receptive fields. To this end, we propose a multi-scale adaptive weighted network (MSAWN) for polarization computational imaging super-resolution to gain superior reconstruction performance. Computational imaging methods centered on information acquisition and interpretation can obtain high-resolution images that are superior Article Title to imaging systems. Specifically, we use a limited amount of memory and computational power even with multi-scale information and multi-level polarization. Secondly, a spatial pyramid structure based on space-channel attention mechanism is designed to effectively adjust the feature weight of polarization information. Thirdly, we adopt an adaptive weight unit to reduce redundant network branches and parameters. Particularly, we introduce an innovative reconstruction layer with inputs coming from multiple paths by mean of sub-pixel convolution. The experimental results show that the proposed method achieves better reconstruction accuracy and visual effect, and the objective evaluation indexes such as PSNR and SSIM are significantly improved.

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Section: Introductionmentioning

confidence: 99%

Multi-scale adaptive weighted network for polarization computational imaging super-resolution

Wang

Zhang

et al. 2022

Appl. Phys. B

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“…Another collaboration between a Canadian and two German Institutes studied the influence of external AC fields on magnetic flux trapping of matter in a superconducting state by polarized neutron imaging [8]. Additionally, from the Berlin University of Applied Sciences, Germany, the problem of determination of the spatial resolution of polarized neutrons was investigated mathematically and experimentally [9]. An Italian-Swiss-Korean collaboration used neutron imaging for the microstructural characterization of a single crystal copper rod using monochromatic neutron radiography and tomography [10].…”

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confidence: 99%