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J. Imaging, Volume 5, Issue 5 (May 2019)

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Cover Story (view full-size image) Artificial light at night (ALAN) exerts significant stress on natural environments around the [...] Read more.
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Open AccessArticle
Measurements of Night Sky Brightness in the Veneto Region of Italy: Sky Quality Meter Network Results and Differential Photometry by Digital Single Lens Reflex
J. Imaging 2019, 5(5), 56; https://doi.org/10.3390/jimaging5050056
Received: 3 April 2019 / Revised: 17 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
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Abstract
In this paper, we present the implementation of a monitoring network for artificial light at night (ALAN), based on Sky Quality Meter devices (SQM) installed in seven locations of the Veneto region. The system is coordinated by the Regional Environmental Protection Agency (ARPA-Veneto) [...] Read more.
In this paper, we present the implementation of a monitoring network for artificial light at night (ALAN), based on Sky Quality Meter devices (SQM) installed in seven locations of the Veneto region. The system is coordinated by the Regional Environmental Protection Agency (ARPA-Veneto) and the Department of Physics and Astronomy of the University of Padova, in collaboration with a local dark-sky association, Venetostellato. A new centralized database containing zenith night sky brightness (NSB) data was implemented to collect data from all SQM stations of the regional territory, not only in real time (since 2017), but in some stations since 2011. We now have a dataset to determine how light pollution is affecting astronomical observatories. A WEB portal was created to offer different downloads from these NSB data. We present the results of some elaborations for the 2018 dataset (statistics, histograms, annual and cumulative plots) for seven monitoring sites. For Ekar and Pennar sites, we also present the NSB monthly trend from 2014 until the time of the study. We purchased a reflex camera with a fish eye lens, appropriately calibrated with the software (SW) Sky Quality Camera, which allowed us to study ALAN using differential photometry. Here, we present our first results obtained by studying the night evolution of light pollution in the urban location of Padova. Full article
(This article belongs to the Special Issue Light Pollution Assessment with Imaging Devices)
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Open AccessArticle
Novel Stopping Criteria for Optimization-Based Microwave Breast Imaging Algorithms
J. Imaging 2019, 5(5), 55; https://doi.org/10.3390/jimaging5050055
Received: 25 April 2019 / Accepted: 14 May 2019 / Published: 22 May 2019
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Abstract
A discontinuous Galerkin formulation of the Contrast Source Inversion algorithm (DGM-CSI) for microwave breast imaging employing a frequency-cycling reconstruction technique has been modified here to include a set of automated stopping criteria that determine a suitable time to shift imaging frequencies and to [...] Read more.
A discontinuous Galerkin formulation of the Contrast Source Inversion algorithm (DGM-CSI) for microwave breast imaging employing a frequency-cycling reconstruction technique has been modified here to include a set of automated stopping criteria that determine a suitable time to shift imaging frequencies and to globally terminate the reconstruction. Recent studies have explored the use of tissue-dependent geometrical mapping of the well-reconstructed real part to its imaginary part as initial guesses during consecutive frequency hops. This practice was shown to improve resulting 2D images of the dielectric properties of synthetic breast models, but a fixed number of iterations was used to halt DGM-CSI inversions arbitrarily. Herein, a new set of stopping conditions is introduced based on an intelligent statistical analysis of a window of past iterations of data error using the two-sample Kolmogorov-Smirnov (K-S) test. This non-parametric goodness-of-fit test establishes a pattern in the data error distribution, indicating an appropriate time to shift frequencies, or terminate the algorithm. The proposed stopping criteria are shown to improve the efficiency of DGM-CSI while yielding images of equivalent quality to assigning an often liberally overestimated number of iterations per reconstruction. Full article
(This article belongs to the Special Issue Microwave Imaging and Electromagnetic Inverse Scattering Problems)
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Open AccessArticle
Methods for Assessment and Monitoring of Light Pollution around Ecologically Sensitive Sites
J. Imaging 2019, 5(5), 54; https://doi.org/10.3390/jimaging5050054
Received: 10 April 2019 / Revised: 7 May 2019 / Accepted: 10 May 2019 / Published: 18 May 2019
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Abstract
Since the introduction of electric lighting over a century ago, and particularly in the decades following the Second World War, indications of artificial light on the nighttime Earth as seen from Earth orbit have increased at a rate exceeding that of world population [...] Read more.
Since the introduction of electric lighting over a century ago, and particularly in the decades following the Second World War, indications of artificial light on the nighttime Earth as seen from Earth orbit have increased at a rate exceeding that of world population growth during the same period. Modification of the natural photic environment at night is a clear and imminent consequence of the proliferation of anthropogenic light at night into outdoor spaces, and with this unprecedented change comes a host of known and suspected ecological consequences. In the past two decades, the conservation community has gradually come to view light pollution as a threat requiring the development of best management practices. Establishing those practices demands a means of quantifying the problem, identifying polluting sources, and monitoring the evolution of their impacts through time. The proliferation of solid-state lighting and the changes to source spectral power distribution it has brought relative to legacy lighting technologies add the complication of color to the overall situation. In this paper, I describe the challenge of quantifying light pollution threats to ecologically-sensitive sites in the context of efforts to conserve natural nighttime darkness, assess the current state of the art in detection and imaging technology as applied to this realm, review some recent innovations, and consider future prospects for imaging approaches to provide substantial support for darkness conservation initiatives around the world. Full article
(This article belongs to the Special Issue Light Pollution Assessment with Imaging Devices)
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Open AccessEditorial
Image Processing Using FPGAs
J. Imaging 2019, 5(5), 53; https://doi.org/10.3390/jimaging5050053
Received: 6 May 2019 / Revised: 7 May 2019 / Accepted: 7 May 2019 / Published: 10 May 2019
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Abstract
Nine articles have been published in this Special Issue on image processing using field programmable gate arrays (FPGAs). The papers address a diverse range of topics relating to the application of FPGA technology to accelerate image processing tasks. The range includes: Custom processor [...] Read more.
Nine articles have been published in this Special Issue on image processing using field programmable gate arrays (FPGAs). The papers address a diverse range of topics relating to the application of FPGA technology to accelerate image processing tasks. The range includes: Custom processor design to reduce the programming burden; memory management for full frames, line buffers, and image border management; image segmentation through background modelling, online K-means clustering, and generalised Laplacian of Gaussian filtering; connected components analysis; and visually lossless image compression. Full article
(This article belongs to the Special Issue Image Processing Using FPGAs) Printed Edition available
Open AccessReview
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
J. Imaging 2019, 5(5), 52; https://doi.org/10.3390/jimaging5050052
Received: 9 April 2019 / Revised: 29 April 2019 / Accepted: 2 May 2019 / Published: 8 May 2019
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Abstract
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging [...] Read more.
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
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Open AccessArticle
A Minimum Rank Approach for Reduction of Environmental Noise in Near-Field Array Antenna Diagnosis
J. Imaging 2019, 5(5), 51; https://doi.org/10.3390/jimaging5050051
Received: 5 February 2019 / Revised: 26 April 2019 / Accepted: 28 April 2019 / Published: 2 May 2019
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Abstract
A method to filter out the contribution of interference sources in array diagnosis is proposed. The interference-affected near field measured on a surface is treated as a (complex-data) image. This allows to use some modern image processing algorithms. In particular, two strategies widely [...] Read more.
A method to filter out the contribution of interference sources in array diagnosis is proposed. The interference-affected near field measured on a surface is treated as a (complex-data) image. This allows to use some modern image processing algorithms. In particular, two strategies widely used in image processing are applied. The first one is the reduction of the amount of information by acquiring only the innovation part of an image, as currently happens in video processing. More specifically, a differential measurement technique is used to formulate the estimation of the array excitations as a sparse recovery problem. The second technique has been recently proposed in video denoising, where the image is split into a low-rank and high-rank part. In particular, in this paper the interference field is filtered out using sparsity as discriminant adopting a mixed minimum 1 norm and trace norm minimization algorithm. The methodology can be applied to both near and far field measurement ranges. It could be an alternative to the systematic use of anechoic chambers for antenna array testing. Full article
(This article belongs to the Special Issue Microwave Imaging and Electromagnetic Inverse Scattering Problems)
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Open AccessArticle
A JND-Based Pixel-Domain Algorithm and Hardware Architecture for Perceptual Image Coding
J. Imaging 2019, 5(5), 50; https://doi.org/10.3390/jimaging5050050
Received: 29 March 2019 / Accepted: 16 April 2019 / Published: 26 April 2019
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Abstract
This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual image coding architecture, which is based on downsampling [...] Read more.
This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual image coding architecture, which is based on downsampling and predictive coding. The downsampling is performed adaptively on the input image based on regions-of-interest (ROIs) identified by measuring the downsampling distortions against the visibility thresholds given by the JND model. The coding error at any pixel location can be guaranteed to be within the corresponding JND threshold in order to obtain excellent visual quality. Experimental results show the improved accuracy of the proposed JND model in estimating visual redundancies compared with classic JND models published earlier. Compression experiments demonstrate improved rate-distortion performance and visual quality over JPEG-LS as well as reduced compressed bit rates compared with other standard codecs such as JPEG 2000 at the same peak signal-to-perceptible-noise ratio (PSPNR). FPGA synthesis results targeting a mid-range device show very moderate hardware resource requirements and over 100 Megapixel/s throughput of both the JND model and the perceptual encoder. Full article
(This article belongs to the Special Issue Image Processing Using FPGAs) Printed Edition available
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J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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