6 research outputs found
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
Due to the lack of natural scene and haze prior information, it is greatly
challenging to completely remove the haze from single image without distorting
its visual content. Fortunately, the real-world haze usually presents
non-homogeneous distribution, which provides us with many valuable clues in
partial well-preserved regions. In this paper, we propose a Non-Homogeneous
Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph
reasoning. Firstly, we employ the gamma correction iteratively to simulate
artificial multiple shots under different exposure conditions, whose haze
degrees are different and enrich the underlying scene prior. Secondly, beyond
utilizing the local neighboring relationship, we build a bidimensional graph
reasoning module to conduct non-local filtering in the spatial and channel
dimensions of feature maps, which models their long-range dependency and
propagates the natural scene prior between the well-preserved nodes and the
nodes contaminated by haze. We evaluate our method on different benchmark
datasets. The results demonstrate that our method achieves superior performance
over many state-of-the-art algorithms for both the single image dehazing and
hazy image understanding tasks
A Comparison of Image Denoising Methods
The advancement of imaging devices and countless images generated everyday
pose an increasingly high demand on image denoising, which still remains a
challenging task in terms of both effectiveness and efficiency. To improve
denoising quality, numerous denoising techniques and approaches have been
proposed in the past decades, including different transforms, regularization
terms, algebraic representations and especially advanced deep neural network
(DNN) architectures. Despite their sophistication, many methods may fail to
achieve desirable results for simultaneous noise removal and fine detail
preservation. In this paper, to investigate the applicability of existing
denoising techniques, we compare a variety of denoising methods on both
synthetic and real-world datasets for different applications. We also introduce
a new dataset for benchmarking, and the evaluations are performed from four
different perspectives including quantitative metrics, visual effects, human
ratings and computational cost. Our experiments demonstrate: (i) the
effectiveness and efficiency of representative traditional denoisers for
various denoising tasks, (ii) a simple matrix-based algorithm may be able to
produce similar results compared with its tensor counterparts, and (iii) the
notable achievements of DNN models, which exhibit impressive generalization
ability and show state-of-the-art performance on various datasets. In spite of
the progress in recent years, we discuss shortcomings and possible extensions
of existing techniques. Datasets, code and results are made publicly available
and will be continuously updated at
https://github.com/ZhaomingKong/Denoising-Comparison.Comment: In this paper, we intend to collect and compare various denoising
methods to investigate their effectiveness, efficiency, applicability and
generalization ability with both synthetic and real-world experiment
Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations
The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementaci贸n sistem谩tica de la telemedicina dentro de un gran centro de evaluaci贸n de COVID-19 en el 谩rea de Baja California, M茅xico. Nuestro modelo se basa en factores de dise帽o centrados en el ser humano y colaboraciones interdisciplinarias para la habilitaci贸n escalable basada en datos de tecnolog铆as de teleconsulta de tel茅fonos inteligentes, celulares y video para vincular hospitales, cl铆nicas y servicios m茅dicos de emergencia para evaluaciones de COVID en el punto de atenci贸n. pruebas, y para el tratamiento posterior y decisiones de cuarentena. R谩pidamente se cre贸 un equipo multidisciplinario, en cooperaci贸n con diferentes instituciones, entre ellas: la Universidad Aut贸noma de Baja California, la Secretar铆a de Salud, el Centro de Comando, Comunicaciones y Control Inform谩tico.
de la Secretar铆a del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psic贸logos. Nuestro objetivo es proporcionar informaci贸n al p煤blico y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignaci贸n de recursos con la anticipaci贸n de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
Gaze-Based Human-Robot Interaction by the Brunswick Model
We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered