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Статті в журналах з теми "Low-light images"
Patil, Akshay, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, and Rupali Bora. "Low Light Image Enhancement for Dark Images." International Journal of Data Science and Analysis 6, no. 4 (2020): 99. http://dx.doi.org/10.11648/j.ijdsa.20200604.11.
Повний текст джерелаHu, Zhe, Sunghyun Cho, Jue Wang, and Ming-Hsuan Yang. "Deblurring Low-Light Images with Light Streaks." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 10 (October 1, 2018): 2329–41. http://dx.doi.org/10.1109/tpami.2017.2768365.
Повний текст джерелаYang, Yi, Zhengguo Li, and Shiqian Wu. "Low-Light Image Brightening via Fusing Additional Virtual Images." Sensors 20, no. 16 (August 17, 2020): 4614. http://dx.doi.org/10.3390/s20164614.
Повний текст джерелаFENG Wei, 冯. 维., 吴贵铭 WU Gui-ming, 赵大兴 ZHAO Da-xing, and 刘红帝 LIU Hong-di. "Multi images fusion Retinex for low light image enhancement." Optics and Precision Engineering 28, no. 3 (2020): 736–44. http://dx.doi.org/10.3788/ope.20202803.0736.
Повний текст джерелаLee, Hosang. "Successive Low-Light Image Enhancement Using an Image-Adaptive Mask." Symmetry 14, no. 6 (June 6, 2022): 1165. http://dx.doi.org/10.3390/sym14061165.
Повний текст джерелаXu, Xin, Shiqin Wang, Zheng Wang, Xiaolong Zhang, and Ruimin Hu. "Exploring Image Enhancement for Salient Object Detection in Low Light Images." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (March 31, 2021): 1–19. http://dx.doi.org/10.1145/3414839.
Повний текст джерелаHuang, Haofeng, Wenhan Yang, Yueyu Hu, Jiaying Liu, and 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.
Повний текст джерелаWang, Yufei, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, and Alex Kot. "Low-Light Image Enhancement with Normalizing Flow." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2604–12. http://dx.doi.org/10.1609/aaai.v36i3.20162.
Повний текст джерелаMatsui, Sosuke, Takahiro Okabe, Mihoko Shimano, and 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.
Повний текст джерелаCao, Shuning, Yi Chang, Shengqi Xu, Houzhang Fang, and Luxin Yan. "Nonlinear Deblurring for Low-Light Saturated Image." Sensors 23, no. 8 (April 7, 2023): 3784. http://dx.doi.org/10.3390/s23083784.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаSankaran, 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.
Повний текст джерелаАвраменко, Віктор Васильович, Виктор Васильевич Авраменко, Viktor Vasylovych Avramenko, and 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.
Повний текст джерелаLandin, 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.
Повний текст джерелаDatorseende 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.
Повний текст джерелаMiller, 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.
Повний текст джерелаZhao, Ping. "Low-Complexity Deep Learning-Based Light Field Image Quality Assessment." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25977.
Повний текст джерелаAnzagira, Leo. "Imaging performance in advanced small pixel and low light image sensors." Thesis, Dartmouth College, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10144602.
Повний текст джерелаEven 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.
Повний текст джерелаRaventos, Joaquin. "New Test Set for Video Quality Benchmarking." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etd/1226.
Повний текст джерелаКниги з теми "Low-light images"
The low light photography field guide: Go beyond daylight to capture stunning low light images. Lewes, East Sussex: ILEX, 2011.
Знайти повний текст джерелаThe low light photography field guide: Go beyond daylight to capture stunning low light images. Waltham, MA: Focal Press/Elsevier, 2011.
Знайти повний текст джерелаB, Johnson C., Sinha Divyendu, Laplante Phillip A, Society of Photo-optical Instrumentation Engineers., and 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.
Знайти повний текст джерелаScrofani, James William. An adaptive method for the enhanced fusion of low-light visible and uncooled thermal infrared imagery. Monterey, Calif: Naval Postgraduate School, 1997.
Знайти повний текст джерелаMaster Low Light Photography: Create Beautiful Images from Twilight to Dawn. Amherst Media, Incorporated, 2016.
Знайти повний текст джерелаFreeman, Michael. Low Light Photography Field Guide: The Essential Guide to Getting Perfect Images in Challenging Light. Taylor & Francis Group, 2014.
Знайти повний текст джерелаAn Adaptive Method for the Enhanced Fusion of Low-Light Visible and Uncooled Thermal Infrared Imagery. Storming Media, 1997.
Знайти повний текст джерелаVanacker, Beatrijs, and Lieke van Deinsen, eds. Portraits and Poses. Leuven University Press, 2022. http://dx.doi.org/10.11116/9789461664532.
Повний текст джерелаЧастини книг з теми "Low-light images"
Gogineni, Navyadhara, Yashashvini Rachamallu, Rineeth Saladi, and K. V. V. Bhanu Prakash. "Image Caption Generation for Low Light Images." In 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.
Повний текст джерелаRollin, Joël. "Optics for Images at Low Light Levels." In Optics in Instruments, 235–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118744321.ch8.
Повний текст джерелаKavya, Avvaru Greeshma, Uruguti Aparna, and Pallikonda Sarah Suhasini. "Enhancement of Low-Light Images Using CNN." In 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.
Повний текст джерелаMatsui, Sosuke, Takahiro Okabe, Mihoko Shimano, and Yoichi Sato. "Image Enhancement of Low-Light Scenes with Near-Infrared Flash Images." In Computer Vision – ACCV 2009, 213–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12307-8_20.
Повний текст джерелаXu, Chenmin, Shijie Hao, Yanrong Guo, and Richang Hong. "Enhancing Low-Light Images with JPEG Artifact Based on Image Decomposition." In 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.
Повний текст джерелаBai, Lianfa, Jing Han, and Jiang Yue. "Colourization of Low-Light-Level Images Based on Rule Mining." In Night Vision Processing and Understanding, 235–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1669-2_8.
Повний текст джерелаSun, Jianing, Jiaao Zhang, Risheng Liu, and Fan Xin. "Brightening the Low-Light Images via a Dual Guided Network." In Artificial Intelligence, 240–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_21.
Повний текст джерелаLi, Mading, Jiaying Liu, Wenhan Yang, and Zongming Guo. "Joint Denoising and Enhancement for Low-Light Images via Retinex Model." In Communications in Computer and Information Science, 91–99. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8108-8_9.
Повний текст джерелаJian, Wuzhen, Hui Zhao, Zhe Bai, and Xuewu Fan. "Low-Light Remote Sensing Images Enhancement Algorithm Based on Fully Convolutional Neural Network." In 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.
Повний текст джерелаGhosh, Archan, Kalporoop Goswami, Riju Chatterjee, and Paramita Sarkar. "A Light SRGAN for Up-Scaling of Low Resolution and High Latency Images." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Low-light images"
Hu, Zhe, Sunghyun Cho, Jue Wang, and Ming-Hsuan Yang. "Deblurring Low-Light Images with Light Streaks." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.432.
Повний текст джерелаCagigal, Manuel P., and Pedro M. Prieto. "Low-light-level images reconstruction." In EI 92, edited by James R. Sullivan, Benjamin M. Dawson, and Majid Rabbani. SPIE, 1992. http://dx.doi.org/10.1117/12.58331.
Повний текст джерелаCagigal, Manuel P., and Pedro M. Prieto. "Recovery from low light level images." In Education in Optics. SPIE, 1992. http://dx.doi.org/10.1117/12.57882.
Повний текст джерелаMpouziotas, Dimitrios, Eleftherios Mastrapas, Nikos Dimokas, Petros Karvelis, and Evripidis Glavas. "Object Detection for Low Light Images." In 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.
Повний текст джерелаCheng, B. T., M. A. Fiddy, J. D. Newman, R. C. Van Vranken, and D. L. Clark. "Image restoration from low light level degraded data." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/qlip.1989.tuc4.
Повний текст джерелаZhuo, Shaojie, Xiaopeng Zhang, Xiaoping Miao, and Terence Sim. "Enhancing low light images using near infrared flash images." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5652900.
Повний текст джерелаHayashi, Masahiro, Fumihiko Sakaue, Jun Sato, Yoshiteru Koreeda, Masakatsu Higashikubo, and Hidenori Yamamoto. "Recovering High Intensity Images from Sequential Low Light Images." In 17th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010891600003124.
Повний текст джерелаWernick, Miles N., and G. Michael Morris. "Image Classification at Low Light Levels." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/qlip.1986.tud2.
Повний текст джерелаPuzovic, Snezana, Ranko Petrovic, Milos Pavlovic, and Srdan Stankovic. "Enhancement Algorithms for Low-Light and Low-Contrast Images." In 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH). IEEE, 2020. http://dx.doi.org/10.1109/infoteh48170.2020.9066316.
Повний текст джерелаIsberg, Thomas A., and G. Michael Morris. "Rotation-Invariant image recognition at low light levels." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tur4.
Повний текст джерелаЗвіти організацій з теми "Low-light images"
Sinai, Michael J., Jason S. McCarley, and William K. Krebs. Scene Recognition with Infrared, Low-Light, and Sensor-Fused Imagery. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada389643.
Повний текст джерела