Literatura académica sobre el tema "Low-light images"
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Artículos de revistas sobre el tema "Low-light images"
Patil, Akshay, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane y Rupali Bora. "Low Light Image Enhancement for Dark Images". International Journal of Data Science and Analysis 6, n.º 4 (2020): 99. http://dx.doi.org/10.11648/j.ijdsa.20200604.11.
Texto completoHu, Zhe, Sunghyun Cho, Jue Wang y Ming-Hsuan Yang. "Deblurring Low-Light Images with Light Streaks". IEEE Transactions on Pattern Analysis and Machine Intelligence 40, n.º 10 (1 de octubre de 2018): 2329–41. http://dx.doi.org/10.1109/tpami.2017.2768365.
Texto completoYang, Yi, Zhengguo Li y Shiqian Wu. "Low-Light Image Brightening via Fusing Additional Virtual Images". Sensors 20, n.º 16 (17 de agosto de 2020): 4614. http://dx.doi.org/10.3390/s20164614.
Texto completoFENG Wei, 冯. 维., 吴贵铭 WU Gui-ming, 赵大兴 ZHAO Da-xing y 刘红帝 LIU Hong-di. "Multi images fusion Retinex for low light image enhancement". Optics and Precision Engineering 28, n.º 3 (2020): 736–44. http://dx.doi.org/10.3788/ope.20202803.0736.
Texto completoLee, Hosang. "Successive Low-Light Image Enhancement Using an Image-Adaptive Mask". Symmetry 14, n.º 6 (6 de junio de 2022): 1165. http://dx.doi.org/10.3390/sym14061165.
Texto completoXu, Xin, Shiqin Wang, Zheng Wang, Xiaolong Zhang y Ruimin Hu. "Exploring Image Enhancement for Salient Object Detection in Low Light Images". ACM Transactions on Multimedia Computing, Communications, and Applications 17, n.º 1s (31 de marzo de 2021): 1–19. http://dx.doi.org/10.1145/3414839.
Texto completoHuang, Haofeng, Wenhan Yang, Yueyu Hu, Jiaying Liu y Ling-Yu Duan. "Towards Low Light Enhancement With RAW Images". IEEE Transactions on Image Processing 31 (2022): 1391–405. http://dx.doi.org/10.1109/tip.2022.3140610.
Texto completoWang, Yufei, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau y Alex Kot. "Low-Light Image Enhancement with Normalizing Flow". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junio de 2022): 2604–12. http://dx.doi.org/10.1609/aaai.v36i3.20162.
Texto completoMatsui, Sosuke, Takahiro Okabe, Mihoko Shimano y Yoichi Sato. "Image Enhancement of Low-light Scenes with Near-infrared Flash Images". IPSJ Transactions on Computer Vision and Applications 2 (2010): 215–23. http://dx.doi.org/10.2197/ipsjtcva.2.215.
Texto completoCao, Shuning, Yi Chang, Shengqi Xu, Houzhang Fang y Luxin Yan. "Nonlinear Deblurring for Low-Light Saturated Image". Sensors 23, n.º 8 (7 de abril de 2023): 3784. http://dx.doi.org/10.3390/s23083784.
Texto completoTesis sobre el tema "Low-light images"
McKoen, K. M. H. H. "Digital restoration of low light level video images". Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343720.
Texto completoSankaran, Sharlini. "The influence of ambient light on the detectability of low-contrast lesions in simulated ultrasound images". Ohio : Ohio University, 1999. http://www.ohiolink.edu/etd/view.cgi?ohiou1175627273.
Texto completoАвраменко, Віктор Васильович, Виктор Васильевич Авраменко, Viktor Vasylovych Avramenko y К. Salnik. "Recognition of fragments of standard images at low light level and the presence of additive impulsive noise". Thesis, Sumy State University, 2017. http://essuir.sumdu.edu.ua/handle/123456789/55739.
Texto completoLandin, Roman. "Object Detection with Deep Convolutional Neural Networks in Images with Various Lighting Conditions and Limited Resolution". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300055.
Texto completoDatorseende är en nyckelkomponent i alla autonoma system. Applikationer för datorseende i realtid är beroende av en korrekt detektering och klassificering av objekt. En detekteringsalgoritm som inte kan garantera rimlig noggrannhet är inte tillämpningsbar i realtidsscenarier, där huvudmålet är säkerhet. Faktorer som påverkar detekteringsnoggrannheten är belysningförhållanden och bildupplösning. Dessa bidrar till degradering av objekt och leder till låg klassificerings- och detekteringsnoggrannhet. Senaste utvecklingar av Convolutional Neural Networks (CNNs) -baserade algoritmer erbjuder möjligheter för förbättring av bilder med dålig belysning och bildgenerering med superupplösning vilket gör det möjligt att kombinera sådana modeller för att förbättra bildkvaliteten och öka detekteringsnoggrannheten. I denna uppsats utvärderas olika CNN-modeller för superupplösning och förbättring av bilder med dålig belysning genom att jämföra genererade bilder med det faktiska data. För att kvantifiera inverkan av respektive modell på detektionsnoggrannhet utvärderades en detekteringsprocedur på genererade bilder. Experimentella resultat utvärderades på bilder utvalda från NoghtOwls och Caltech datauppsättningar för fotgängare och visade att bildgenerering med superupplösning och bildförbättring i svagt ljus förbättrar noggrannheten med en betydande marginal. Dessutom har det bevisats att en kaskad av superupplösning-generering och förbättring av bilder med dålig belysning ytterligare ökar noggrannheten. Den största nackdelen med sådana kaskader är relaterad till en ökad beräkningstid som begränsar möjligheterna för en rad realtidsapplikationer.
Vorhies, John T. "Low-complexity Algorithms for Light Field Image Processing". University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1590771210097321.
Texto completoMiller, Sarah Victoria. "Mulit-Resolution Aitchison Geometry Image Denoising for Low-Light Photography". University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1596444315236623.
Texto completoZhao, Ping. "Low-Complexity Deep Learning-Based Light Field Image Quality Assessment". Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25977.
Texto completoAnzagira, Leo. "Imaging performance in advanced small pixel and low light image sensors". Thesis, Dartmouth College, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10144602.
Texto completoEven though image sensor performance has improved tremendously over the years, there are two key areas where sensor performance leaves room for improvement. Firstly, small pixel performance is limited by low full well, low dynamic range and high crosstalk, which greatly impact the sensor performance. Also, low light color image sensors, which use color filter arrays, have low sensitivity due to the selective light rejection by the color filters. The quanta image sensor (QIS) concept was proposed to mitigate the full well and dynamic range issues in small pixel image sensors. In this concept, spatial and temporal oversampling is used to address the full well and dynamic range issues. The QIS concept however does not address the issue of crosstalk. In this dissertation, the high spatial and temporal oversampling of the QIS concept is leveraged to enhance small pixel performance in two ways. Firstly, the oversampling allows polarization sensitive QIS jots to be incorporated to obtain polarization information. Secondly, the oversampling in the QIS concept allows the design of alternative color filter array patterns for mitigating the impact of crosstalk on color reproduction in small pixels. Finally, the problem of performing color imaging in low light conditions is tackled with a proposed stacked pixel concept. This concept which enables color sampling without the use of absorption color filters, improves low light sensitivity. Simulations are performed to demonstrate the advantage of this proposed pixel structure over sensors employing color filter arrays such as the Bayer pattern. A color correction algorithm for improvement of color reproduction in low light is also developed and demonstrates improved performance.
Hurle, Bernard Alfred. "The charge coupled device as a low light detector in beam foil spectroscopy". Thesis, University of Kent, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332296.
Texto completoRaventos, Joaquin. "New Test Set for Video Quality Benchmarking". Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etd/1226.
Texto completoLibros sobre el tema "Low-light images"
The low light photography field guide: Go beyond daylight to capture stunning low light images. Lewes, East Sussex: ILEX, 2011.
Buscar texto completoThe low light photography field guide: Go beyond daylight to capture stunning low light images. Waltham, MA: Focal Press/Elsevier, 2011.
Buscar texto completoB, Johnson C., Sinha Divyendu, Laplante Phillip A, Society of Photo-optical Instrumentation Engineers. y Boeing Company, eds. Low-light-level and real-time imaging systems, components, and applications: 9-11 July 2002, Seattle, Washington, USA. Bellingham, Wash., USA: SPIE, 2003.
Buscar texto completoScrofani, James William. An adaptive method for the enhanced fusion of low-light visible and uncooled thermal infrared imagery. Monterey, Calif: Naval Postgraduate School, 1997.
Buscar texto completoMaster Low Light Photography: Create Beautiful Images from Twilight to Dawn. Amherst Media, Incorporated, 2016.
Buscar texto completoFreeman, Michael. Low Light Photography Field Guide: The Essential Guide to Getting Perfect Images in Challenging Light. Taylor & Francis Group, 2014.
Buscar texto completoAn Adaptive Method for the Enhanced Fusion of Low-Light Visible and Uncooled Thermal Infrared Imagery. Storming Media, 1997.
Buscar texto completoVanacker, Beatrijs y Lieke van Deinsen, eds. Portraits and Poses. Leuven University Press, 2022. http://dx.doi.org/10.11116/9789461664532.
Texto completoCapítulos de libros sobre el tema "Low-light images"
Gogineni, Navyadhara, Yashashvini Rachamallu, Rineeth Saladi y K. V. V. Bhanu Prakash. "Image Caption Generation for Low Light Images". En Communications in Computer and Information Science, 57–72. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20977-2_5.
Texto completoRollin, Joël. "Optics for Images at Low Light Levels". En Optics in Instruments, 235–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118744321.ch8.
Texto completoKavya, Avvaru Greeshma, Uruguti Aparna y Pallikonda Sarah Suhasini. "Enhancement of Low-Light Images Using CNN". En Emerging Research in Computing, Information, Communication and Applications, 1–9. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1342-5_1.
Texto completoMatsui, Sosuke, Takahiro Okabe, Mihoko Shimano y Yoichi Sato. "Image Enhancement of Low-Light Scenes with Near-Infrared Flash Images". En Computer Vision – ACCV 2009, 213–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12307-8_20.
Texto completoXu, Chenmin, Shijie Hao, Yanrong Guo y Richang Hong. "Enhancing Low-Light Images with JPEG Artifact Based on Image Decomposition". En Advances in Multimedia Information Processing – PCM 2018, 3–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00767-6_1.
Texto completoBai, Lianfa, Jing Han y Jiang Yue. "Colourization of Low-Light-Level Images Based on Rule Mining". En Night Vision Processing and Understanding, 235–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1669-2_8.
Texto completoSun, Jianing, Jiaao Zhang, Risheng Liu y Fan Xin. "Brightening the Low-Light Images via a Dual Guided Network". En Artificial Intelligence, 240–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_21.
Texto completoLi, Mading, Jiaying Liu, Wenhan Yang y Zongming Guo. "Joint Denoising and Enhancement for Low-Light Images via Retinex Model". En Communications in Computer and Information Science, 91–99. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8108-8_9.
Texto completoJian, Wuzhen, Hui Zhao, Zhe Bai y Xuewu Fan. "Low-Light Remote Sensing Images Enhancement Algorithm Based on Fully Convolutional Neural Network". En Proceedings of the 5th China High Resolution Earth Observation Conference (CHREOC 2018), 56–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6553-9_7.
Texto completoGhosh, Archan, Kalporoop Goswami, Riju Chatterjee y Paramita Sarkar. "A Light SRGAN for Up-Scaling of Low Resolution and High Latency Images". En Communications in Computer and Information Science, 56–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81462-5_6.
Texto completoActas de conferencias sobre el tema "Low-light images"
Hu, Zhe, Sunghyun Cho, Jue Wang y Ming-Hsuan Yang. "Deblurring Low-Light Images with Light Streaks". En 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.432.
Texto completoCagigal, Manuel P. y Pedro M. Prieto. "Low-light-level images reconstruction". En EI 92, editado por James R. Sullivan, Benjamin M. Dawson y Majid Rabbani. SPIE, 1992. http://dx.doi.org/10.1117/12.58331.
Texto completoCagigal, Manuel P. y Pedro M. Prieto. "Recovery from low light level images". En Education in Optics. SPIE, 1992. http://dx.doi.org/10.1117/12.57882.
Texto completoMpouziotas, Dimitrios, Eleftherios Mastrapas, Nikos Dimokas, Petros Karvelis y Evripidis Glavas. "Object Detection for Low Light Images". En 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). IEEE, 2022. http://dx.doi.org/10.1109/seeda-cecnsm57760.2022.9932921.
Texto completoCheng, B. T., M. A. Fiddy, J. D. Newman, R. C. Van Vranken y D. L. Clark. "Image restoration from low light level degraded data". En Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/qlip.1989.tuc4.
Texto completoZhuo, Shaojie, Xiaopeng Zhang, Xiaoping Miao y Terence Sim. "Enhancing low light images using near infrared flash images". En 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5652900.
Texto completoHayashi, Masahiro, Fumihiko Sakaue, Jun Sato, Yoshiteru Koreeda, Masakatsu Higashikubo y Hidenori Yamamoto. "Recovering High Intensity Images from Sequential Low Light Images". En 17th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010891600003124.
Texto completoWernick, Miles N. y G. Michael Morris. "Image Classification at Low Light Levels". En Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/qlip.1986.tud2.
Texto completoPuzovic, Snezana, Ranko Petrovic, Milos Pavlovic y Srdan Stankovic. "Enhancement Algorithms for Low-Light and Low-Contrast Images". En 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH). IEEE, 2020. http://dx.doi.org/10.1109/infoteh48170.2020.9066316.
Texto completoIsberg, Thomas A. y G. Michael Morris. "Rotation-Invariant image recognition at low light levels". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tur4.
Texto completoInformes sobre el tema "Low-light images"
Sinai, Michael J., Jason S. McCarley y William K. Krebs. Scene Recognition with Infrared, Low-Light, and Sensor-Fused Imagery. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1999. http://dx.doi.org/10.21236/ada389643.
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