Literatura académica sobre el tema "Low-light enhancement and denoising"
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Artículos de revistas sobre el tema "Low-light enhancement and denoising"
Carré, Maxime y Michel Jourlin. "Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising". Sensors 21, n.º 23 (27 de noviembre de 2021): 7906. http://dx.doi.org/10.3390/s21237906.
Texto completoZhang, Jialiang, Ruiwen Ji, Jingwen Wang, Hongcheng Sun y Mingye Ju. "DEGAN: Decompose-Enhance-GAN Network for Simultaneous Low-Light Image Lightening and Denoising". Electronics 12, n.º 14 (11 de julio de 2023): 3038. http://dx.doi.org/10.3390/electronics12143038.
Texto completoKim, Minjae, Dubok Park, David Han y Hanseok Ko. "A novel approach for denoising and enhancement of extremely low-light video". IEEE Transactions on Consumer Electronics 61, n.º 1 (febrero de 2015): 72–80. http://dx.doi.org/10.1109/tce.2015.7064113.
Texto completoMalik, Sameer y Rajiv Soundararajan. "A low light natural image statistical model for joint contrast enhancement and denoising". Signal Processing: Image Communication 99 (noviembre de 2021): 116433. http://dx.doi.org/10.1016/j.image.2021.116433.
Texto completoDas Mou, Trisha, Saadia Binte Alam, Md Hasibur Rahman, Gautam Srivastava, Mahady Hasan y Mohammad Faisal Uddin. "Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation". Applied Sciences 13, n.º 2 (12 de enero de 2023): 1034. http://dx.doi.org/10.3390/app13021034.
Texto completoHan, Guang, Yingfan Wang, Jixin Liu y Fanyu Zeng. "Low-light images enhancement and denoising network based on unsupervised learning multi-stream feature modeling". Journal of Visual Communication and Image Representation 96 (octubre de 2023): 103932. http://dx.doi.org/10.1016/j.jvcir.2023.103932.
Texto completoHu, Linshu, Mengjiao Qin, Feng Zhang, Zhenhong Du y Renyi Liu. "RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images". Remote Sensing 13, n.º 1 (26 de diciembre de 2020): 62. http://dx.doi.org/10.3390/rs13010062.
Texto completoZhao, Meng Ling y Min Xia Jiang. "Research on Enhanced of Mine-Underground Picture". Advanced Materials Research 490-495 (marzo de 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.548.
Texto completoWu, Zeju, Yang Ji, Lijun Song y Jianyuan Sun. "Underwater Image Enhancement Based on Color Correction and Detail Enhancement". Journal of Marine Science and Engineering 10, n.º 10 (17 de octubre de 2022): 1513. http://dx.doi.org/10.3390/jmse10101513.
Texto completoXu, Xiaogang, Ruixing Wang, Chi-Wing Fu y Jiaya Jia. "Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 3 (26 de junio de 2023): 3054–62. http://dx.doi.org/10.1609/aaai.v37i3.25409.
Texto completoTesis sobre el tema "Low-light enhancement and denoising"
Dalasari, Venkata Gopi Krishna y Sri Krishna Jayanty. "Low Light Video Enhancement along with Objective and Subjective Quality Assessment". Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13500.
Texto completoBagchi, Deblin. "Transfer learning approaches for feature denoising and low-resource speech recognition". The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577641434371497.
Texto completoLi, Meng. "Low-Observable Object Detection and Tracking Using Advanced Image Processing Techniques". University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1396465762.
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 completoChikkamadal, Manjunatha Prathiksha. "Aitchison Geometry and Wavelet Based Joint Demosaicking and Denoising for Low Light Imaging". University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1627842487059544.
Texto completoCASULA, ESTER ANNA RITA. "Low mass dimuon production with the ALICE muon spectrometer". Doctoral thesis, Università degli Studi di Cagliari, 2014. http://hdl.handle.net/11584/266451.
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.
Chen, Hsueh-I. y 陳學儀. "Deep Burst Low Light Image Enhancement with Alignment, Denoising and Blending". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sfk685.
Texto completo國立臺灣大學
資訊網路與多媒體研究所
106
Taking photos under low light environment is always a challenge for most camera. In this thesis, we propose a neural network pipeline for processing burst short-exposure raw data. Our method contains alignment, denoising and blending. First, we use FlowNet2.0 to predict the optical flow between burst images and align these burst images. And then, we feed the aligned burst raw data into a DenoiseUNet, which includes denoise-part and color-part, to generate an RGB image. Finally, we use a MaskUNet to generate a mask that can distinguish misalignment. We blend the outputs from single raw image and from burst raw images by the mask. Our method proves that using burst inputs has significantly improvement than single input.
Malik, Sameer. "Low Light Image Restoration: Models, Algorithms and Learning with Limited Data". Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6120.
Texto completoWang, Szu-Chieh y 王思傑. "Extreme Low Light Image Enhancement with Generative Adversarial Networks". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/cz8pqb.
Texto completo國立臺灣大學
資訊工程學研究所
107
Taking photos under low light environments is always a challenge for current imaging pipelines. Image noise and artifacts corrupt the image. Tak- ing the great success of deep learning into consideration recently, it may be straightforward to train a deep convolutional network to perform enhance- ment on such images to restore the underlying clean image. However, the large number of parameters in deep models may require a large amount of data to train. For the low light image enhancement task, paired data requires a short exposure image and a long exposure image to be taken with perfect alignment, which may not be achievable in every scene, thus limiting the choice of possible scenes to capture paired data and increasing the effort to collect training data. Also, data-driven solutions tend to replace the entire camera pipeline and cannot be easily integrated to existing pipelines. There- fore, we propose to handle the task with our 2-stage pipeline, consisting of an imperfect denoise network, and a bias correction net BC-UNet. Our method only requires noisy bursts of short exposure images and unpaired long expo- sure images, relaxing the effort of collecting training data. Also, our method works in raw domain and is capable of being easily integrated into the ex- isting camera pipeline. Our method achieves comparable improvements to other methods under the same settings.
Libros sobre el tema "Low-light enhancement and denoising"
Hong, M. H. Laser applications in nanotechnology. Editado por A. V. Narlikar y Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.013.24.
Texto completoCapítulos de libros sobre el tema "Low-light enhancement and denoising"
Li, 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 completoVogt, Carson, Geng Lyu y Kartic Subr. "Lightless Fields: Enhancement and Denoising of Light-Deficient Light Fields". En Advances in Visual Computing, 383–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_30.
Texto completoWang, Haodian, Yang Wang, Yang Cao y Zheng-Jun Zha. "Fusion-Based Low-Light Image Enhancement". En MultiMedia Modeling, 121–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27077-2_10.
Texto completoFotiadou, Konstantina, Grigorios Tsagkatakis y Panagiotis Tsakalides. "Low Light Image Enhancement via Sparse Representations". En Lecture Notes in Computer Science, 84–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11758-4_10.
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 completoSong, Juan, Liang Zhang, Peiyi Shen, Xilu Peng y Guangming Zhu. "Single Low-Light Image Enhancement Using Luminance Map". En Communications in Computer and Information Science, 101–10. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3005-5_9.
Texto completoTu, Juanjuan, Zongliang Gan y Feng Liu. "Retinex Based Flicker-Free Low-Light Video Enhancement". En Pattern Recognition and Computer Vision, 367–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_31.
Texto completoNie, Xixi, Zilong Song, Bing Zhou y Yating Wei. "LESN: Low-Light Image Enhancement via Siamese Network". En Lecture Notes in Electrical Engineering, 183–94. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6963-7_17.
Texto completoPanwar, Moomal y Sanjay B. C. Gaur. "Inception-Based CNN for Low-Light Image Enhancement". En Computational Vision and Bio-Inspired Computing, 533–45. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9573-5_39.
Texto completoPriyadarshini, R., Arvind Bharani, E. Rahimankhan y N. Rajendran. "Low-Light Image Enhancement Using Deep Convolutional Network". En Innovative Data Communication Technologies and Application, 695–705. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9651-3_57.
Texto completoActas de conferencias sobre el tema "Low-light enhancement and denoising"
Feng, Hansen, Lizhi Wang, Yuzhi Wang y Hua Huang. "Learnability Enhancement for Low-light Raw Denoising". En MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548186.
Texto completoGuo, Yu, Yuxu Lu, Meifang Yang y Ryan Wen Liu. "Low-light Image Enhancement with Deep Blind Denoising". En ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383972.3384022.
Texto completoGao, Yin, Chao Yan, Huixiong Zeng, Qiming Li y Jun Li. "Adaptive Low-Light Image Enhancement with Decomposition Denoising". En 2022 7th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 2022. http://dx.doi.org/10.1109/icrae56463.2022.10056212.
Texto completoLim, Jaemoon, Jin-Hwan Kim, Jae-Young Sim y Chang-Su Kim. "Robust contrast enhancement of noisy low-light images: Denoising-enhancement-completion". En 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351583.
Texto completoSong, Yuda, Yunfang Zhu y Xin Du. "Automatical Enhancement and Denoising of Extremely Low-light Images". En 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412195.
Texto completoWenshuai Yin, Xiangbo Lin y Yi Sun. "A novel framework for low-light colour image enhancement and denoising". En 2011 3rd International Conference on Awareness Science and Technology (iCAST). IEEE, 2011. http://dx.doi.org/10.1109/icawst.2011.6163088.
Texto completoYu, Long, Haonan Su y Cheolkon Jung. "Joint Enhancement And Denoising of Low Light Images Via JND Transform". En ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053027.
Texto completoWang, Jikun, Weixiang Liang, Xianbo Wang y Zhixin Yang. "An Image Denoising and Enhancement Approach for Dynamic Low-light Environment". En 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2022. http://dx.doi.org/10.1109/ipec54454.2022.9777630.
Texto completoZhou, Dewei, Zongxin Yang y Yi Yang. "Pyramid Diffusion Models for Low-light Image Enhancement". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/199.
Texto completoLi, Lin, Ronggang Wang, Wenmin Wang y Wen Gao. "A low-light image enhancement method for both denoising and contrast enlarging". En 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351501.
Texto completoInformes sobre el tema "Low-light enhancement and denoising"
Birkmire, Robert, Juejun Hu y Kathleen Richardson. Beyond the Lambertian limit: Novel low-symmetry gratings for ultimate light trapping enhancement in next-generation photovoltaics. Office of Scientific and Technical Information (OSTI), mayo de 2016. http://dx.doi.org/10.2172/1419008.
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