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J. Imaging, Volume 5, Issue 11 (November 2019)

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Cover Story (view full-size image) The spatial resolution and light detected in fluorescence imaging for small animals are limited by [...] Read more.
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Open AccessArticle
Comparing Radar-Based Breast Imaging Algorithm Performance with Realistic Patient-Specific Permittivity Estimation
J. Imaging 2019, 5(11), 87; https://doi.org/10.3390/jimaging5110087 - 19 Nov 2019
Abstract
Radar-based breast imaging has shown promise as an imaging modality for early-stage cancer detection, and clinical investigations of two commercial imaging systems are ongoing. Many imaging algorithms have been proposed, which seek to improve the quality of the reconstructed microwave image to enhance [...] Read more.
Radar-based breast imaging has shown promise as an imaging modality for early-stage cancer detection, and clinical investigations of two commercial imaging systems are ongoing. Many imaging algorithms have been proposed, which seek to improve the quality of the reconstructed microwave image to enhance the potential clinical decision. However, in many cases, the radar-based imaging algorithms have only been tested in limited numerical or experimental test cases or with simplifying assumptions such as using one estimate of permittivity for all patient test cases. In this work, the potential impact of patient-specific permittivity estimation on algorithm comparison is highlighted using representative experimental breast phantoms. In particular, the case studies presented help show that the permittivity estimate can impact the conclusions of the algorithm comparison. Overall, this work suggests that it is important that imaging algorithm comparisons use realistic test cases with and without breast abnormalities and with reconstruction permittivity estimation. Full article
(This article belongs to the Special Issue Microwave Imaging and Electromagnetic Inverse Scattering Problems)
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Open AccessArticle
Usage of Vertical Fisheye-Images to Quantify Urban Light Pollution on Small Scales and the Impact of LED Conversion
J. Imaging 2019, 5(11), 86; https://doi.org/10.3390/jimaging5110086 - 18 Nov 2019
Abstract
The aim of this work was to develop an easy and quick technique for characterizing various lighting situations, that is, single lamps or illuminated signs and to quantify impacts on small scales like streets, buildings and near areas. The method uses a DSLR-camera [...] Read more.
The aim of this work was to develop an easy and quick technique for characterizing various lighting situations, that is, single lamps or illuminated signs and to quantify impacts on small scales like streets, buildings and near areas. The method uses a DSLR-camera equipped with fisheye-lens and the software Sky Quality Camera, both commonly used as part of night sky imagery in the light pollution community, to obtain information about luminance and correlated colour temperature. As a difference to its usual build-up, observed light emitting sources were captured by pointing the camera towards analysed objects, that is, images were taken via vertical plane imaging with very short exposure times under one second. Results have proven that this technique provides a practical way to quantify the lighting efficacy in a certain place or area, as a quantitative analysis of the direct emission towards the observer and the illumination on surroundings, that is, street surfaces, sidewalks and buildings, was performed. When conducting lamp conversions, the method can be used to characterize the gradient of change and could be a useful tool for municipalities to find the optimal lighting solution. The paper shows examples of different lighting situations like single lamps of different types, also containing various luminaires, illuminated billboards or buildings and impacts of the lighting transition to LEDs in the city of Eisenstadt, Austria. The horizontal fisheye method is interdisciplinary applicable, for example, being suitable for lighting management, to sustainability and energy saving purposes. Full article
(This article belongs to the Special Issue Light Pollution Assessment with Imaging Devices)
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Open AccessArticle
Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions
J. Imaging 2019, 5(11), 85; https://doi.org/10.3390/jimaging5110085 - 15 Nov 2019
Abstract
This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS [...] Read more.
This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package. Full article
(This article belongs to the Special Issue Multispectral Imaging)
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Open AccessEditorial
The Future of Hyperspectral Imaging
J. Imaging 2019, 5(11), 84; https://doi.org/10.3390/jimaging5110084 - 25 Oct 2019
Abstract
The Special Issue on hyperspectral imaging (HSI), entitled “The Future of Hyperspectral Imaging”, has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments [...] Read more.
The Special Issue on hyperspectral imaging (HSI), entitled “The Future of Hyperspectral Imaging”, has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI. Some contributions also update specific methodological or mathematical tools. In particular, the review papers address deep learning methods for HSI analysis, while HSI data compression is reviewed by using liquid crystals spectral multiplexing as well as DMD-based Raman spectroscopy. Specific topics explored by using data obtained by HSI include alert on the sprouting of potato tubers, the investigation on the stability of painting samples, the prediction of healing diabetic foot ulcers, and age determination of blood-stained fingerprints. Papers showing advances on more general topics include video approach for HSI dynamic scenes, localization of plant diseases, new methods for the lossless compression of HSI data, the fusing of multiple multiband images, and mixed modes of laser HSI imaging for sorting and quality controls. Full article
(This article belongs to the Special Issue The Future of Hyperspectral Imaging) Printed Edition available
Open AccessArticle
Numerical Simulation of a Scanning Illumination System for Deep Tissue Fluorescence Imaging
J. Imaging 2019, 5(11), 83; https://doi.org/10.3390/jimaging5110083 - 24 Oct 2019
Abstract
The spatial resolution and light detected in fluorescence imaging for small animals are limited by light scattering, absorption and autofluorescence. To address this, novel near-infrared fluorescent contrast agents and imaging configurations have been investigated. In this paper, the influence of the light wavelength [...] Read more.
The spatial resolution and light detected in fluorescence imaging for small animals are limited by light scattering, absorption and autofluorescence. To address this, novel near-infrared fluorescent contrast agents and imaging configurations have been investigated. In this paper, the influence of the light wavelength and imaging configurations (full-field illumination system and scanning system) on fluorescence imaging are compared quantitatively. The surface radiance for both systems is calculated by modifying the simulation tool Near-Infrared Fluorescence and Spectral Tomography. Fluorescent targets are embedded within a scattering medium at different positions. The surface radiance and spatial resolution are obtained for emission wavelengths between 620 nm and 1000 nm. It was found that the spatial resolution of the scanning system is independent of the tissue optical properties, whereas for full-field illumination, the spatial resolution degrades at longer wavelength. The full width at half maximum obtained by the scanning system is 25% lower than that obtained by the full-field illumination system when the targets are located in the middle of the phantom. The results indicate that although imaging at near-infrared wavelength can achieve a higher surface radiance, it may produce worse spatial resolution. Full article
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