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Deblurring low-light images with events / Chu Zhou in International journal of computer vision, vol 131 n° 5 (May 2023)  

Public [article]  inInternational journal of computer vision > vol 131 n° 5 (May 2023) . - pp 1284 - 1298 Titre : Deblurring low-light images with events Type de document : Article/Communication Auteurs : Chu Zhou, Auteur ; Minggui Teng, Auteur ; Jin Han, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1284 - 1298 Note générale : bilbiographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] caméra d'événement [Termes IGN] correction d'image [Termes IGN] filtrage du bruit [Termes IGN] flou [Termes IGN] image à basse résolution [Termes IGN] image RVB Résumé : (auteur) Modern image-based deblurring methods usually show degenerate performance in low-light conditions since the images often contain most of the poorly visible dark regions and a few saturated bright regions, making the amount of effective features that can be extracted for deblurring limited. In contrast, event cameras can trigger events with a very high dynamic range and low latency, which hardly suffer from saturation and naturally encode dense temporal information about motion. However, in low-light conditions existing event-based deblurring methods would become less robust since the events triggered in dark regions are often severely contaminated by noise, leading to inaccurate reconstruction of the corresponding intensity values. Besides, since they directly adopt the event-based double integral model to perform pixel-wise reconstruction, they can only handle low-resolution grayscale active pixel sensor images provided by the DAVIS camera, which cannot meet the requirement of daily photography. In this paper, to apply events to deblurring low-light images robustly, we propose a unified two-stage framework along with a motion-aware neural network tailored to it, reconstructing the sharp image under the guidance of high-fidelity motion clues extracted from events. Besides, we build an RGB-DAVIS hybrid camera system to demonstrate that our method has the ability to deblur high-resolution RGB images due to the natural advantages of our two-stage framework. Experimental results show our method achieves state-of-the-art performance on both synthetic and real-world images. Numéro de notice : A2023-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-023-01754-5 Date de publication en ligne : 06/02/2023 En ligne : https://doi.org/10.1007/s11263-023-01754-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103062 [article]

Event-driven feature detection and tracking for visual SLAM / Ignacio Alzugaray (2022)  

Public Titre : Event-driven feature detection and tracking for visual SLAM Type de document : Thèse/HDR Auteurs : Ignacio Alzugaray, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2022 Note générale : bibliographie thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] caméra d'événement [Termes IGN] cartographie et localisation simultanées [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] image floue [Termes IGN] reconnaissance de formes [Termes IGN] séquence d'images [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (auteur) Traditional frame-based cameras have become the de facto sensor of choice for a multitude of applications employing Computer Vision due to their compactness, low cost, ubiquity, and ability to provide information-rich exteroceptive measurements. Despite their dominance in the field, these sensors exhibit limitations in common, real-world scenarios where detrimental effects, such as motion blur during high-speed motion or over-/underexposure in scenes with poor illumination, are prevalent. Challenging the dominance of traditional cameras, the recent emergence of bioinspired event cameras has opened up exciting research possibilities for robust perception due to their high-speed sensing, High-Dynamic-Range capabilities, and low power consumption. Despite their promising characteristics, event cameras present numerous challenges due to their unique output: a sparse and asynchronous stream of events, only capturing incremental perceptual changes at individual pixels. This radically different sensing modality renders most of the traditional Computer Vision algorithms incompatible without substantial prior adaptation, as they are initially devised for processing sequences of images captured at fixed frame-rate. Consequently, the bulk of existing event-based algorithms in the literature have opted to discretize the event stream into batches and process them sequentially, effectively reverting to frame-like representations in an attempt to mimic the processing of image sequences from traditional sensors. Such event-batching algorithms have demonstrably outperformed other alternative frame-based algorithms in scenarios where the quality of conventional intensity images is severely compromised, unveiling the inherent potential of these new sensors and popularizing them. To date, however, many newly designed event-based algorithms still rely on a contrived discretization of the event stream for its processing, suggesting that the full potential of event cameras is yet to be harnessed by processing their output more naturally. This dissertation departs from the mere adaptation of traditional frame-based approaches and advocates instead for the development of new algorithms integrally designed for event cameras to fully exploit their advantageous characteristics. In particular, the focus of this thesis lies on describing a series of novel strategies and algorithms that operate in a purely event-driven fashion, \ie processing each event as soon as it gets generated without any intermediate buffering of events into arbitrary batches and thus avoiding any additional latency in their processing. Such event-driven processes present additional challenges compared to their simpler event-batching counterparts, which, in turn, can largely be attributed to the requirement to produce reliable results at event-rate, entailing significant practical implications for their deployment in real-world applications. The body of this thesis addresses the design of event-driven algorithms for efficient and asynchronous feature detection and tracking with event cameras, covering alongside crucial elements on pattern recognition and data association for this emerging sensing modality. In particular, a significant portion of this thesis is devoted to the study of visual corners for event cameras, leading to the design of innovative event-driven approaches for their detection and tracking as corner-events. Moreover, the presented research also investigates the use of generic patch-based features and their event-driven tracking for the efficient retrieval of high-quality feature tracks. All the developed algorithms in this thesis serve as crucial stepping stones towards a completely event-driven, feature-based Simultaneous Localization And Mapping (SLAM) pipeline. This dissertation extends upon established concepts from state-of-the-art, event-driven methods and further explores the limits of the event-driven paradigm in realistic monocular setups. While the presented approaches solely rely on event-data, the gained insights are seminal to future investigations targeting the combination of event-based vision with other, complementary sensing modalities. The research conducted here paves the way towards a new family of event-driven algorithms that operate efficiently, robustly, and in a scalable manner, envisioning a potential paradigm shift in event-based Computer Vision. Note de contenu : 1- Introduction 2- Contribution 3- Conclusion and outlook Numéro de notice : 28699 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : ETH Zurich : 2022 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/541700 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100470

EMVS : Event-based Multi-View Stereo : 3D reconstruction with an event camera in real-time / Henri Rebecq in International journal of computer vision, vol 126 n° 12 (December 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 12 (December 2018) . - pp 1394 - 1414 Titre : EMVS : Event-based Multi-View Stereo : 3D reconstruction with an event camera in real-time Type de document : Article/Communication Auteurs : Henri Rebecq, Auteur ; Guillermo Gallego, Auteur ; Elias Mueggler, Auteur ; Davide Scaramuzza, Auteur Année de publication : 2018 Article en page(s) : pp 1394 - 1414 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] caméra d'événement [Termes IGN] carte de profondeur [Termes IGN] luminance lumineuse [Termes IGN] reconstruction 3D [Termes IGN] temps réel Résumé : (Auteur) Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that a paradigm shift is needed. We introduce the problem of event-based multi-view stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (1) its ability to respond to scene edges—which naturally provide semi-dense geometric information without any pre-processing operation—and (2) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semi-dense depth maps, without requiring any explicit data association or intensity estimation. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU. Numéro de notice : A2018-597 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1050-6 Date de publication en ligne : 07/11/2017 En ligne : https://doi.org/10.1007/s11263-017-1050-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92524 [article]