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14th International Conference on Digital image processing, ICDIP 2022

作者： Emmendorfer, Leonardo R. de Olviveira, Vinicius Menezes Schein, Tatiana T. de Jesus, Junior C. Rodriguez, Bárbara D.A. Electrical Energy Processing Department Federal University of Santa Maria 97105-340 Brazil Center for Computational Science Federal University of Rio Grande 96201-900 Brazil Computational Modeling PhD Program Federal University of Rio Grande 96201-900 Brazil Computer Engineering Graduate Program Federal University of Rio Grande 96201-900 Brazil Institute of Mathematics Statistics and Physics Federal University of Rio Grande 96201-900 Brazil

ISBN: (纸本)9781510657564

Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to alleviate this type of artifact. However, the actual effectiveness of this approach is not clear-cut in the literature. This work evaluates the effect of three simple sharpening filters on the quality of images obtained from super-resolution methods. Two metrics were considered in the evaluation: the Peak signal-to-noise ratio (PSNR) metric, and the Learned Perceptual image Patch Similarity (LPIPS). One of the filters could consistently improve the LPIPS metric of magnified images from diverse benchmark sets on top of seven super-resolution methods. The increments obtained for the perceptual metric seem to occur due to the sharpening effect. Improvements on PSNR values were not as consistent. © 2022 SPIE.

关键词： Quality control

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1D quasi-bound states in the continuum with large operation bandwidth in theω~k space for nonlinear optical applications

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Photonics Research 2022年第7期10卷 1575-1581页

作者： KAILI SUN HUI JIANG DMITRY ABYKOV VIEN VAN URIEL LEVY YANGJIAN CAI ZHANGHUA HAN Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and ApplicationsSchool of Physics and ElectronicsShandong Normal UniversityJinan 250358China Image Processing Systems Institute—Branch of the Federal Scientific Research Centre“Crystallography and Photonics”of Russian Academy of Sciences Samara 443001Russia Department of Electrical and Computer Engineering University of AlbertaEdmontonAlberta T6G 2V4Canada Department of Applied Physics and Center for Nanoscience and NanotechnologyThe Hebrew University of JerusalemJerusalemIsrael

The phenomenon of bound state in the continuum(BIC)with an infinite quality factor and lifetime has emerged in recent years in photonics as a new tool to manipulate light-matter ***,most of the investigated structures only support BIC resonances at very few discrete points in theω~k *** when the BIC is switched to a quasi-BIC(QBIC)resonance through perturbation,its frequency will still be located within a narrow spectral band close to that of the original BIC,restricting their applications in many fields where random or multiple input frequencies beyond the narrow band are *** this work,we demonstrate that a new set of QBIC resonances can be supported by using a special binary grating consisting of two alternatingly aligned ridge arrays with the same period and zero-approaching ridge width difference on a slab *** QBIC resonances are distributed continuously over a broad band along a line in theω~k space and can thus be considered as 1 D *** the Q factors generally affected by the ridge difference,it is now possible to arbitrarily choose any frequencies on the dispersion line to achieve significantly enhanced light-matter interactions,facilitating many applications where multiple input wavelengths are required;e.g.,sum or difference frequency generations in nonlinear optics.

关键词： quasi nonlinear narrow

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Adversarial semantic contour for object detection

arXiv

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arXiv 2021年

作者： Zhang, Yichi Zhu, Zijian Yang, Xiao Zhu, Jun Department of Computer Science and Technology Institute for AI THBI Lab Tsinghua University Beijing100084 China Institute of Image Processing and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

Modern object detectors are vulnerable to adversarial examples, which brings potential risks to numerous applications, e.g., self-driving car. Among attacks regularized by lp norm, l0-attack aims to modify as few pixels as possible. Nevertheless, the problem is nontrivial since it generally requires to optimize the shape along with the texture simultaneously, which is an NP-hard problem. To address this issue, we propose a novel method of Adversarial Semantic Contour (ASC) guided by object contour as prior. With this prior, we reduce the searching space to accelerate the l0 optimization, and also introduce more semantic information which should affect the detectors more. Based on the contour, we optimize the selection of modified pixels via sampling and their colors with gradient descent alternately. Extensive experiments demonstrate that our proposed ASC outperforms the most commonly manually designed patterns (e.g., square patches and grids) on task of disappearing. By modifying no more than 5% and 3.5% of the object area respectively, our proposed ASC can successfully mislead the mainstream object detectors including the SSD512, Yolov4, Mask RCNN, Faster RCNN, etc. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Incoherent Digital Holography using Spiral Rotating Point Spread Functions Created by Double-helix Beams

Incoherent Digital Holography using Spiral Rotating Point Sp...

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Digital Holography and Three-Dimensional Imaging, DH 2022

作者： Dubey, Nitin Anand, Vijayakumar Khonina, Svetlana Kumar, Ravi Reddy, Andra Naresh Kumar Rosen, Joseph School of Electrical and Computer Engineering Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva8410501 Israel Institute of Physics University of Tartu Tartu50411 Estonia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn MelbourneVIC3122 Australia Samara National Research University Samara443086 Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences Samara443001 Russia Hee Photonic Labs RigaLV-1002 Latvia Department of Physics of Complex Systems Weizmann Institute of Science Rehovot7610001 Israel

A new incoherent 3D imaging system with a rotating point spread function has been developed. Different computational reconstruction methods such as non-linear reconstruction and the Lucy-Richardson-Rosen algorithm wer... 详细信息

ISBN: (纸本)9781957171128

关键词： Optical transfer function

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A Multi-Level Thresholding image Segmentation Based on an Improved Artificial Bee Colony Algorithm 2nd

A Multi-Level Thresholding Image Segmentation Based on an Im...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Xia, Xingyu Gao, Hao Hu, Haidong Lan, Rushi Pun, Chi-Man The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer and Information Science University of Macau China Beijing Institute of Control Engineering Beijing China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030177621

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： image segmentation

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A Gröbner Basis Approach to Combinatorial Nullstellensatz

arXiv

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arXiv 2023年

作者： Xu, Yang Kan, Haibin Han, Guangyue Department of Mathematics Faculty of Science The University of Hong Kong Pokfulam Road Hong Kong Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Engineering Research Center of Blockchain Shanghai200433 China Yiwu Research Institute Fudan University Zhejiang Yiwu City322000 China

In this paper, using some conditions that arise naturally in Alon's combinatorial Nullstellensatz as well as its various extensions and generalizations, we characterize Gröbner bases consisting of monic polynomials, which helps us to establish a Nullstellensatz from a Gröbner basis perspective. As corollaries of this general Nullstellensatz, we establish four special Nullstellensatz, which, among others, include a common generalization of the Nullstellensatz for multisets established in Kós, Rónyai and Mészáros [23, 24] and the Nullstellensatz with multiplicity established in Ball and Serra [9], and include a punctured Nullstellensatz, generalizing several existing results in the literature. As applications of our punctured Nullstellensatz, we extend some results on hyperplane covering in [9, 23, 24] to wider settings, and give an alternative proof of the generalized Alon-Füredi theorem established in Bishnoi, Clark, Potukuchi and Schmitt [12]. Unless specified otherwise, all our results are established over an arbitrary commutative ring R. © 2023, CC BY.

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging

arXiv

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arXiv 2025年

作者： Stevens, Tristan S.W. Overdevest, Jeroen Nolan, Oisín van Nierop, Wessel L. van Sloun, Ruud J.G. Eldar, Yonina C. Department of Electrical Engineering University of Technology Eindhoven Netherlands Signal Processing Algorithms NXP Semiconductors Netherlands Faculty of Mathematics and Computer Science Weizmann institute of Science Israel

Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution. In recent years, generative modeling for Bayesian inference on sensory data has also gained traction. Nevertheless, the direct application of generative modeling techniques initially designed for natural images on raw sensory data is not straightforward, requiring solutions that deal with high dynamic range signals acquired from multiple sensors or arrays of sensors that interfere with each other, and that typically acquire data at a very high rate. Moreover, the exact physical data-generating process is often complex or unknown. As a consequence, approximate models are used, resulting in discrepancies between model predictions and the observations that are non-Gaussian, in turn complicating the Bayesian inverse problem. Finally, sensor data is often used in real-time processing or decision-making systems, imposing stringent requirements on, e.g., latency and throughput. In this paper, we will discuss some of these challenges and offer approaches to address them, all in the context of high-rate real-time sensing applications in automotive radar and medical imaging. © The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://***/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. © 2025, CC BY-SA.

关键词： Inverse problems

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Generalization properties of hyper-RKHS and its applications

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 6220-6257页

作者： Fanghui Liu Lei Shi Xiaolin Huang Jie Yang Johan A.K. Suykens Department of Electrical Engineering ESAT-STADIUS KU Leuven Leuven Belgium Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai China Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view. Algorithmically, we consider two regularized regression models with bivariate forms in this space, including kernel ridge regression (KRR) and support vector regression (SVR) endowed with hyper-RKHS, and further combine divide-and-conquer with Nyström approximation for scalability in large sample cases. This framework is general: the underlying kernel is learned from a broad class, and can be positive definite or not, which adapts to various requirements in kernel learning. Theoretically, we study the convergence behavior of regularized regression algorithms in hyper-RKHS and derive the learning rates, which goes beyond the classical analysis on RKHS due to the non-trivial independence of pairwise samples and the characterisation of hyper-RKHS. Experimentally, results on several benchmarks suggest that the employed framework is able to learn a general kernel function form an arbitrary similarity matrix, and thus achieves a satisfactory performance on classification tasks.

关键词： hyper-RKHS approximation theory kernel learning out-of-sample extensions

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Model for Analysis of High-Frequency Modulated Random Signals

Model for Analysis of High-Frequency Modulated Random Signal...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Ihor Javorskyj Roman Yuzefovych Oleh Lychak Pavlo Semenov Roman Sliepko Yulia Romanyshyn Department of Methods and Facilities for Acquisition and Processing Diagnostic Signals Karpenko Physico-mechanical Institute of NAS of Ukraine Lviv Ukraine Institute of Telecommunication and Computer Sciences Bydgoszcz University of Science and Technology Bydgoszcz Poland Department of Applied Mathematics Lviv Polytechnic National University Lviv Ukraine Department of Hoisting and Transport Machines and Engineering of Port Technological Equipment Odesa National Maritime University Odesa Ukraine Department of Records Management and Information Activity Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine

The use of the Hilbert transform for the analysis of periodically non-stationary random signals (PNRSs), whose carrier harmonics are modulated by jointly stationary high-frequency random processes is discussed. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The properties of the band-pass filtered signals are examined, and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. We show that it is possible to extract the quadratures of narrow-band high-frequency modulation processes using the Hilbert transform.

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AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

JACC: Advances

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JACC: Advances 2024年第10期3卷 101249-101249页

作者： Han, Donghee Shanbhag, Aakash Miller, Robert JH. Kwok, Nicholas Waechter, Parker Builoff, Valerie Newby, David E. Dey, Damini Berman, Daniel S. Slomka, Piotr Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada British Heart Foundation Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom

Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR. © 2024 The Authors

关键词： artificial intelligence cardiac magnetic resonance imaging computed tomography myocardial mass

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