scholarly journals Computing light pollution indicators for environmental assessment

2021 ◽  
Author(s):  
Fabio Falchi ◽  
Salvador Bará
Keyword(s):  
Light Pollution ◽  
Light Sources ◽  
Point Spread ◽  
Main Indicator ◽  
Rotationally Symmetric ◽  
Spread Function ◽  
Speed Up ◽  
Sky Brightness ◽  
Sky Radiance ◽  
Spatially Variant

Light pollution modelling and monitoring has traditionally used zenith sky brightness as its main indicator. Several other indicators (e.g. average sky radiance, horizontal irradiance, average sky radiance at given interval of zenith distances) may be more useful, both for general and for specific purposes of ecology studies, night sky and environmental monitoring. These indicators can be calculated after the whole sky radiance is known with sufficient angular detail. This means, for each site, to integrate the contribution in each direction of the sky of each light source in the radius of hundreds of km. This approach is extremely high time consuming if the mapping is desired for a large territory. Here we present a way to obtain maps of large territories for a large subset of useful indicators, bypassing the need to calculate first the radiance map of the whole sky in each site to obtain from it the desired indicator in that site. For each indicator, a point spread function (PSF) is calculated from the whole sky radiance maps generated by a single source at sufficiently dense number of distances from the observing site. If the PSF is transversally shift-invariant, i.e. if it depends only on the relative position of source and observer, then we can further speed up the map calculation via the use of fast Fourier-transform (FFT). We present here examples of maps for different indicators. Precise results can be calculated for any single site, taking into account the site and light sources altitudes, by means of specific inhomogeneous (spatially-variant) and anisotropic (non rotationally symmetric) PSFs.

scholarly journals Fast signal analysis in Rotating-Polarization CARS microscopy

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Giuseppe de Vito ◽  
Vincenzo Piazza
Keyword(s):  
Raman Spectroscopy ◽  
Point Spread Function ◽  
Signal Analysis ◽  
Point Spread ◽  
Novel Approach ◽  
Spread Function ◽  
Cars Microscopy ◽  
Speed Up ◽  
Orientation Anisotropy ◽  
Anti Stokes

AbstractRotating Polarization Coherent Anti-Stokes Raman Spectroscopy (RP-CARS) is a novel approach to CARS microscopy that takes advantage of polarizationdependent selection rules in order to gain information about molecule orientation anisotropy and direction within the optical point spread function. However, in the original implementation of this technique, the lockin amplifier-based acquisition was quite time demanding. Here we present a new software-based approach that permits a great speed-up in the RP-CARS images acquisition process.

scholarly journals Convolutional Neural Network with Spatial-Variant Convolution Kernel

2020 ◽  
Vol 12 (17) ◽  
pp. 2811
Author(s):  
Yongpeng Dai ◽  
Tian Jin ◽  
Yongkun Song ◽  
Shilong Sun ◽  
Chen Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Point Spread Function ◽  
Radar Image ◽  
Radar Images ◽  
Point Spread ◽  
Spread Function ◽  
Spatially Variant ◽  
The Impact

Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed. However, the point spread function (PSF) in the radar images is sometimes spatially variant and affects the performance of the CNN. We propose the spatial-variant convolutional neural network (SV-CNN) aimed at this problem. It will also perform well in other conditions when there are spatially variant features. The convolutional kernels of the CNN can detect motifs with some distinctive features and are invariant to the local position of the motifs. This makes the convolutional neural networks widely used in image processing fields such as image recognition, handwriting recognition, image super-resolution, and semantic segmentation. They also perform well in radar image enhancement. However, the local position invariant character might not be good for radar image enhancement, when features of motifs (also known as the point spread function in the radar imaging field) vary with the positions. In this paper, we proposed an SV-CNN with spatial-variant convolution kernels (SV-CK). Its function is illustrated through a special application of enhancing the radar images. After being trained using radar images with position-codings as the samples, the SV-CNN can enhance the radar images. Because the SV-CNN reads information of the local position contained in the position-coding, it performs better than the conventional CNN. The advance of the proposed SV-CNN is tested using both simulated and real radar images.

Parallel image restoration with spatially variant point spread function: description and first clinical results

2001 ◽  
Author(s):  
Andrew Shearer ◽  
Gerard Gorman ◽  
Triona O'Doherty ◽  
Wilhelm J. van der Putten ◽  
Peter McCarthy ◽  
...  
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Clinical Results ◽  
Point Spread ◽  
Parallel Image ◽  
Spread Function ◽  
Spatially Variant

A Robust Star Acquisition Algorithm Based on Facet Model

2012 ◽  
Vol 532-533 ◽  
pp. 1747-1751
Author(s):  
Guang Rui Li
Keyword(s):  
Cluster Formation ◽  
Minimum Threshold ◽  
Vector Method ◽  
Point Spread ◽  
Spread Function ◽  
Great Progress ◽  
Speed Up ◽  
Intense Noise ◽  
Performance Evaluation Index ◽  
Facet Model

An efficient and robust star acquisition algorithm based on facet fitting is presented to improve the performance of star sensors. The location of star central pixels can be determined by searching extremum intensity pixels among the point spread function (PSF) of stars, which is well fitted by the cubic facet model. According to extremum theory, the second derivative operators are pre-calculated and the searching process can be completed using convolution operations thrice. Simultaneously, cluster formation is also a time consuming routine, which is accomplished using specific maximum and minimum threshold to speed up it. A variety of experiments are carried out to validate the performance of proposed algorithm, moreover, the performance evaluation index M is presented. The results clearly show that the proposed algorithm makes a great progress than the vector method in time expense and accuracy under intense noise conditions.

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