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Microwave Photonic Synthetic Aperture Radar: systems, experiments, and imaging processing

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2024年 2-16页

作者： Chen, Jianlai Xiong, Rongqi Yu, Hanwen Xu, Gang Li, Ruoming Xing, Mengdao Cent South Univ Changsha 410083 Peoples R China Univ Houston Natl Ctr Airborne Laser Mapping Dept Civil & Environm Engn Houston TX USA Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian Peoples R China Univ Naples Federico II Dept Engn Naples Italy Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Nanjing Univ Coll Engn & Appl Sci Nanjing Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Dept Xian Peoples R China

With the development of synthetic aperture radar (SAR) technology, the resolution of SAR images has been improved from tens of meters to centimeters (currently the highest). However, increasing image resolution from centimeters to millimeters will pose a significant challenge to the hardware and signal processing algorithms of traditional radar systems. Fortunately, the emergence of microwave photonic technology assisted SAR systems in recent years is expected to solve this bottleneck. The system is theoretically capable of achieving a signal bandwidth of 10 GHz or even tens of GHz. However, the rapid increase in resolution will also bring new problems and challenges. For example, i) How to address the adverse effects of system instability under ultra-large signal bandwidth;ii) How to improve the imaging efficiency in the case of a sharp increase in data volume;iii) How to overcome the more complex signal characteristics faced by ultra-high-resolution imaging. Although microwave photonic SAR systems and data processing techniques are not yet sufficiently developed, they have great potential and will be a very hot and cutting-edge research field at present and in the future. In this paper, we will make a comprehensive review, summary, and prospect of the related research on microwave photonic SAR from three aspects of systems, experiments, and imaging processing. The purpose of this study is to provide some guiding suggestions and references for relevant researchers to promote the further development of microwave photonic SAR technology.

关键词： Image enhancement

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Automatic segmentation and quantification of OCT images before and after cataract surgery using deep learning

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APPLIED OPTICS 2024年第20期63卷 5376-5386页

作者： Ruiz-Calvo, Andres Ansah, Derick Celik, Ugur MacRae, Scott Marcos, Susana Martinez-Enriquez, Eduardo Univ Carlos III Madrid Madrid Spain Univ Rochester Flaum Eye Inst Rochester NY 14627 USA CSIC Inst Opt Madrid Madrid Spain Univ Rochester Inst Opt Ctr Visual Sci Flaum Eye Inst Rochester NY 14627 USA

Obtaining quantitative geometry of the anterior segment of the eye, generally from optical coherence tomography (OCT) images, is important to construct 3D computer eye models, used to understand the optical quality of the normal and pathological eye and to improve treatment (for example, selecting the intraocular lens to be implanted in cataract surgery or guiding refractive surgery). An important step to quantify OCT images is segmentation (i.e., finding and labeling the surfaces of interest in the images), which, for the purpose of feeding optical models, needs to be automatic, accurate, robust, and fast. In this work, we designed a segmentation algorithm based on deep learning, which we applied to OCT images from pre- and post-cataract surgery eyes obtained using anterior segment OCT commercial systems. We proposed a feature pyramid network architecture with a pre-trained encoder and trained, validated, and tested the algorithm using 1640 OCT images. We showed that the proposed method outperformed a classical image-processing-based approach in terms of accuracy (from 91.4% to 93.2% accuracy), robustness (decreasing the standard deviation of accuracy across images by a factor of 1.7), and processing time (from 0.48 to 0.34 s/image). We also described a method for the 3D models' construction and their quantification from the segmented images and applied the proposed segmentation/quantification algorithms to quantify 136 new eye measurements (780 images) obtained from OCT commercial systems. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Computer simulation Deep learning Gradient index lenses imaging techniques Laser surgery Magnetic resonance imaging

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HQ-I2IT: Redesign the optimization scheme to improve image quality in CycleGAN-based image translation systems

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IET IMAGE processing 2024年第2期18卷 507-522页

作者： Zhang, Yipeng Hu, Bingliang Huang, Yingying Gao, Chi Yin, Jianfu Wang, Quang Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol 17 Informat Ave Xian 710119 Shaanxi Peoples R China Key Lab Biomed Spect Xian Xian Shaanxi Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing Peoples R China

The image-to-image translation (I2IT) task aims to transform images from the source domain into the specified target domain. State-of-the-art CycleGAN-based translation algorithms typically use cycle consistency loss and latent regression loss to constrain translation. In this work, it is demonstrated that the model parameters constrained by the cycle consistency loss and the latent regression loss are equivalent to optimizing the medians of the data distribution and the generative distribution. In addition, there is a style bias in the translation. This bias interacts between the generator and the style encoder and visually exhibits translation errors, e.g. the style of the generated image is not equal to the style of the reference image. To address these issues, a new I2IT model termed high-quality-I2IT (HQ-I2IT) is proposed. The optimization scheme is redesigned to prevent the model from optimizing the median of the data distribution. In addition, by separating the optimization of the generator and the latent code estimator, the redesigned model avoids error interactions and gradually corrects errors during training, thereby avoiding learning the median of the generated distribution. The experimental results demonstrate that the visual quality of the images produced by HQ-I2IT is significantly improved without changing the generator structure, especially when guided by the reference images. Specifically, the Frechet inception distance on the AFHQ and CelebA-HQ datasets are reduced from 19.8 to 10.2 and from 23.8 to 17.0, respectively. In this work, it is demonstrated that the cycle consistency loss and latent regression loss in CycleGAN-based image translation models can be detrimental to image quality. The optimization scheme of CycleGAN-based image translation systems is redesigned and a new translation model named HQ-I2IT is proposed. Experiments demonstrate that the proposed method can significantly improve image quality and translation ***

关键词： computer animation computer vision image processing

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Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 229卷

作者： Paul, Nijhum Sunil, G. C. Horvath, David Sun, Xin North Dakota State Univ Dept Agr & Biosyst Engn Fargo ND 58108 USA North Dakota State Univ Genom Phen & Bioinformat Program Fargo ND 58108 USA USDA ARS ETSARC Weed & insect Biol unit Fargo ND 58102 USA

Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery data. This review paper aims to present a thorough overview of the state-of-the-art DL technologies for plant stress detection. For this purpose, a systematic literature review was conducted to identify relevant articles for highlighting the technologies and approaches currently employed in the development of a DL-based plant stress detection system, specifically the advancement of image-based data collection systems, image preprocessing techniques, and deep learning algorithms and their applications in plant stress classification, disease detection, and segmentation tasks. Additionally, this review emphasizes the challenges and future directions in collecting and preprocessing image data, model development, and deployment in real-world agricultural settings. Some of the key findings from this review paper are: Training data: (i) Most plant stress detection models have been trained on Red Green Blue (RGB) images;(ii) Data augmentation can increase both the quantity and variation of training data;(iii) Handling multimodal inputs (e. g., image, temperature, humidity) allows the model to leverage information from diverse sources, which can improve prediction accuracy;Model Design and Efficiency: (i) Self-supervised learning (SSL) and Few-shot learning (FSL)-based methods may be better than transfer learning (TL)-based models for classifying plant stress when the number of labeled training images are scarce;(ii) Custom designed DL architectures for a specific stress and plant type can have better performance than the state-of-the-art DL architectures in terms of efficiency, overfitting, and accuracy;(iii) The multi-task learning DL structure reuses most of the network architecture while performing multiple tasks (e.g., estimate stress type and severity) simultaneously, which makes the learning much

关键词： Plant stress Computer vision Deep learning Image processing imaging sensor Precision agriculture

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A Dense-Layered Deep Neural Model-Based Classification of Brain Hemorrhages Using Head Computer Tomography Images

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COGNITIVE COMPUTATION 2023年第3期15卷 1042-1052页

作者： Vidyarthi, Ankit Jaypee Inst Informat Technol Dept CSE & IT Noida India

Artificial Intelligence (AI) refers to the ability to learn, remember, predict, and make an optimal judgment based on Computer-assisted Design (CAD) systems. Traditional CAD algorithms and methods on head CT scans focused on the automatic recognition, segmentation, and classification of the abnormalities. But, these approaches were encountered with several limitations like (i) smaller dataset size, (ii) negative Transfer Learning, and (iii) improper localization. This paper proposed the new dense-layered deep neural model to classify brain hemorrhages using head CT scans. The proposed model is the ten-layered network having dense blocks with skip connections. It uses the cross-chained connection between dense blocks to minimize gradient loss while training. Later, the last layer of the model is extended with Grad-Cam for localization of the affected cell regions. The model performance is evaluated on a dataset of head CT scans of size 427.25GB. The dataset is partitioned into 752,800 images in the training set and 121,232 images in the testing set. The experimentation results achieved an accuracy of 98.32% with a mean logarithmic loss of 0.06487. The average classification accuracy of the proposed model on multiple-class hemorrhages is 98.27%. The experimentation results are found satisfactory having the best AUC-ROC accuracy of 98.32%. The comparative analysis of the model with other traditional deep neural networks proves the efficacy of the model in predicting results. Also, in comparison with other methods, the gained results are found satisfactory with an increase in the accuracy of 1.3%.

关键词： Artificial intelligence Cognitive neuroscience Neural processing Brain hemorrhage Computer tomography imaging Deep learning

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Data acquisition and data analysis in colorimetric sensor arrays

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CHEMOMETRICS AND INTELLIGENT LABORATORY systems 2023年 241卷

作者： Hemmateenejad, Bahram Bordbar, Mohammad Mahdi Shojaeifard, Zahra Shiraz Univ Chem Dept Shiraz Iran Shiraz Univ Med Sci Med & Nat Prod Chem Res Ctr Shiraz Iran

Responses in colorimetric sensor arrays are based on the monitoring color changes of chemical transducers by multichannel spectrophotometers or imaging recorders such as scanners, digital camera and smartphones. The images are then digitized using different image analysis algorithms. The array based methods provide a multidimensional data containing analytical, noise and outlier information. The inconsistent data can be removed or decreased by data pre-processing methods. Based on the goal of researches, the refined data can be processed by the pattern recognition methods for qualitative analysis or a by the multivariate regression models for quantitative analysis. This article explains (i) the mechanism of data collection using different types of readers, (ii) introduces the types and applications of algorithms and software for image digitalization, (iii) represents information about data preprocessing, variable selection and multivariate statistical methods and (iv) evaluates their applications in processing of colorimetric sensor array data published during the last decade.

关键词： Colorimetric sensor array Chemometrics Image analysis Data acquisition Pre-processing

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PACOME: Optimal multi-epoch combination of direct imaging observations for joint exoplanet detection and orbit estimation

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ASTRONOMY & ASTROPHYSICS 2023年 679卷

作者： Dallant, Jules Langlois, Maud Flasseur, Olivier Thiebaut, Eric Univ Lyon 1 Ctr Rech Astrophys Lyon CNRS UMR 5574 ENS Lyon F-69230 St Genis Laval France

Context. Exoplanet detections and characterizations via direct imaging require high contrast and high angular resolution. These requirements are typically pursued by combining (i) cutting-edge instrumental facilities equipped with extreme adaptive optics and coronagraphic systems, (ii) optimized differential imaging to introduce a diversity between the signals of the sought-for objects and that of the star, and (iii) dedicated (post-)processing algorithms to further eliminate the residual stellar ***. With respect to the third technique, substantial efforts have been undertaken over this last decade on the design of more efficient post-processing algorithms. The whole data collection and retrieval processes currently allow to detect massive exoplanets at angular separations greater than a few tenths of au. The performance remains upper-bounded at shorter angular separations due to the lack of diversity induced by the processing of each epoch of observations individually. We aim to propose a new algorithm that is able to combine several observations of the same star by accounting for the Keplerian orbital motion across epochs of the sought-for exoplanets in order to constructively co-add their weak ***. The proposed algorithm, PACOME, integrates an exploration of the plausible orbits of the sought-for objects within an end-to-end statistical detection and estimation formalism. The latter is extended to a multi-epoch combination of the maximum likelihood framework of PACO, which is a post-processing algorithm of single-epoch observations. From this, we derived a reliable multi-epoch detection criterion, interpretable both in terms of probability of detection and of false alarm. In addition, PACOME is able to produce a few plausible estimates of the orbital elements of the detected sources and provide their local error ***. We tested the proposed algorithm on several datasets obtained from the VLT/SPHERE instrument with IRDIS and IFS usin

关键词： instrumentation: high angular resolution techniques: image processing methods: statistical methods: data analysis planets and satellites: detection stars: individual: HR 8799

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Scientific Developments in imaging and Dosimetry for Molecular Radiotherapy

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CLINICAL ONCOLOGY 2021年第2期33卷 117-124页

作者： Bardies, M. Gear, J., I Ctr Rech Cancerol Toulouse UMR 1037 Toulouse France Univ Toulouse III Paul Sabatier INSERM UMR 1037 Toulouse France Royal Marsden NHS Fdn Trust Joint Dept Phys Sutton SM2 5PT Surrey England Inst Canc Res Sutton SM2 5PT Surrey England

Molecular radiotherapy is a rapidly developing field with new vector and isotope combinations continually added to market. As with any radiotherapy treatment, it is vital that the absorbed dose and toxicity profile are adequately characterised. Methodologies for absorbed dose calculations for radiopharmaceuticals were generally developed to characterise stochastic effects and not suited to calculations on a patient-specific basis. There has been substantial scientific and technological development within the field of molecular radiotherapy dosimetry to answer this challenge. The development of imaging systems and advanced processing techniques enable the acquisition of accurate measurements of radioactivity within the body. Activity assessment combined with dosimetric models and radiation transport algorithms make individualised absorbed dose calculations not only feasible, but commonplace in a variety of commercially available software packages. The development of dosimetric parameters beyond the absorbed dose has also allowed the possibility to characterise the effect of irradiation by including biological parameters that account for radiation absorbed dose rates, gradients and spatial and temporal energy distribution heterogeneities. Molecular radiotherapy is in an exciting time of its development and the application of dosimetry in this field can only have a positive influence on its continued progression. (C) 2020 Published by Elsevier Ltd on behalf of The Royal College of Radiologists.

关键词： Absorbed dose calculation dosimetry molecular radiotherapy quantitative imaging

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Scalable Non-Cartesian Magnetic Resonance imaging with R2D2

Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2

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European Signal processing Conference (EUSIPCO)

作者： Yiwei Chen Chao Tang Amir Aghabiglou Chung San Chu Yves Wiaux Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh United Kingdom EPCC University of Edinburgh Edinburgh United Kingdom

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

We propose a new approach for non-Cartesian magnetic resonance image reconstruction. While unrolled architectures provide robustness via data-consistency layers, embedding measurement operators in Deep Neural Network (DNN) can become impractical at large scale. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, are not affected by this limitation and have also proven effective, but their highly iterative nature also affects scalability. To address this scalability challenge, we leverage the “Residual-to-Residual DNN series for high-Dynamic range imaging (R2D2)” approach recently introduced in astronomical imaging. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of DNNs taking the previous iteration's image estimate and associated data residual as inputs. The method can be interpreted as a learned version of the Matching Pursuit algorithm. We demonstrate R2D2 in simulation, considering radial k-space sampling acquisition sequences. Our preliminary results suggest that R2D2 achieves: (i) suboptimal performance compared to its unrolled incarnation R2D2-Net, which is however non-scalable due to the necessary embedding of NUFFT-based data-consistency layers; (ii) superior reconstruction quality to a scalable version of R2D2-Net embedding an FFT-based approximation for data consistency; (iii) superior reconstruction quality to PnP, while only requiring few iterations.

关键词： Training Three-dimensional displays Magnetic resonance imaging Scalability Matching pursuit algorithms Artificial neural networks Signal processing Extraterrestrial measurements Robustness Image reconstruction

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New reconstruction methodology for thorax tomosynthesis based on deep learning 7

New reconstruction methodology for thorax tomosynthesis base...

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7th International Conference on Image Formation in X-Ray Computed Tomography

作者： F Del Cerro, C. Galán, A. García Blas, J. Desco, M. Abella, M. The Dpto. Bioingeniería e Ingeniería Aeroespacial Universidad Carlos III de Madrid The Instituto de Investigación Sanitaria Gregorio Marañón Madrid Spain The Dpto. Arquitectura de Computadores Comunicaciones y Sistemas Universidad Carlos III de Madrid Madrid Spain Madrid Spain Madrid Spain

ISBN: (纸本)9781510656697

Tomosynthesis offers an alternative to planar radiography providing pseudo-tomographic information at a much lower radiation dose than CT. The fact that it cannot convey information about the density poses a major limitation towards the use of tomosynthesis in chest imaging, due to the wide range of pathologies that present an increase in the density of the pulmonary parenchyma. Previous works have attempted to improve image quality through enhanced analytical, iterative algorithms, or including a deep learning-based step in the reconstruction, but the results shown are still far from the quantitative information of a CT. In this work, we propose a reconstruction methodology consisting of a filtered back-projection step followed by post-processing based on Deep Learning to obtain a tomographic image closer to CT. Preliminary results show the potential of the proposed methodology to obtain true tomographic information from tomosynthesis data, which could replace CT scans in applications where the radiation dose is critical. © 2022 SPIE.

关键词： Image enhancement

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