Добірка наукової літератури з теми "Low-light enhancement and denoising"
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Статті в журналах з теми "Low-light enhancement and denoising"
Carré, Maxime, and Michel Jourlin. "Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising." Sensors 21, no. 23 (November 27, 2021): 7906. http://dx.doi.org/10.3390/s21237906.
Повний текст джерелаZhang, Jialiang, Ruiwen Ji, Jingwen Wang, Hongcheng Sun, and Mingye Ju. "DEGAN: Decompose-Enhance-GAN Network for Simultaneous Low-Light Image Lightening and Denoising." Electronics 12, no. 14 (July 11, 2023): 3038. http://dx.doi.org/10.3390/electronics12143038.
Повний текст джерелаKim, Minjae, Dubok Park, David Han, and Hanseok Ko. "A novel approach for denoising and enhancement of extremely low-light video." IEEE Transactions on Consumer Electronics 61, no. 1 (February 2015): 72–80. http://dx.doi.org/10.1109/tce.2015.7064113.
Повний текст джерелаMalik, Sameer, and Rajiv Soundararajan. "A low light natural image statistical model for joint contrast enhancement and denoising." Signal Processing: Image Communication 99 (November 2021): 116433. http://dx.doi.org/10.1016/j.image.2021.116433.
Повний текст джерелаDas Mou, Trisha, Saadia Binte Alam, Md Hasibur Rahman, Gautam Srivastava, Mahady Hasan, and Mohammad Faisal Uddin. "Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation." Applied Sciences 13, no. 2 (January 12, 2023): 1034. http://dx.doi.org/10.3390/app13021034.
Повний текст джерелаHan, Guang, Yingfan Wang, Jixin Liu, and 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 (October 2023): 103932. http://dx.doi.org/10.1016/j.jvcir.2023.103932.
Повний текст джерелаHu, Linshu, Mengjiao Qin, Feng Zhang, Zhenhong Du, and Renyi Liu. "RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images." Remote Sensing 13, no. 1 (December 26, 2020): 62. http://dx.doi.org/10.3390/rs13010062.
Повний текст джерелаZhao, Meng Ling, and Min Xia Jiang. "Research on Enhanced of Mine-Underground Picture." Advanced Materials Research 490-495 (March 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.548.
Повний текст джерелаWu, Zeju, Yang Ji, Lijun Song, and Jianyuan Sun. "Underwater Image Enhancement Based on Color Correction and Detail Enhancement." Journal of Marine Science and Engineering 10, no. 10 (October 17, 2022): 1513. http://dx.doi.org/10.3390/jmse10101513.
Повний текст джерелаXu, Xiaogang, Ruixing Wang, Chi-Wing Fu, and 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, no. 3 (June 26, 2023): 3054–62. http://dx.doi.org/10.1609/aaai.v37i3.25409.
Повний текст джерелаДисертації з теми "Low-light enhancement and denoising"
Dalasari, Venkata Gopi Krishna, and 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.
Повний текст джерелаBagchi, 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.
Повний текст джерелаLi, 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.
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 джерелаChikkamadal, 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.
Повний текст джерелаCASULA, 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.
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.
Chen, Hsueh-I., and 陳學儀. "Deep Burst Low Light Image Enhancement with Alignment, Denoising and Blending." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sfk685.
Повний текст джерела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.
Повний текст джерелаWang, Szu-Chieh, and 王思傑. "Extreme Low Light Image Enhancement with Generative Adversarial Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/cz8pqb.
Повний текст джерела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.
Книги з теми "Low-light enhancement and denoising"
Hong, M. H. Laser applications in nanotechnology. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.013.24.
Повний текст джерелаЧастини книг з теми "Low-light enhancement and denoising"
Li, 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 джерелаVogt, Carson, Geng Lyu, and Kartic Subr. "Lightless Fields: Enhancement and Denoising of Light-Deficient Light Fields." In Advances in Visual Computing, 383–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_30.
Повний текст джерелаWang, Haodian, Yang Wang, Yang Cao, and Zheng-Jun Zha. "Fusion-Based Low-Light Image Enhancement." In MultiMedia Modeling, 121–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27077-2_10.
Повний текст джерелаFotiadou, Konstantina, Grigorios Tsagkatakis, and Panagiotis Tsakalides. "Low Light Image Enhancement via Sparse Representations." In Lecture Notes in Computer Science, 84–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11758-4_10.
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 джерелаSong, Juan, Liang Zhang, Peiyi Shen, Xilu Peng, and Guangming Zhu. "Single Low-Light Image Enhancement Using Luminance Map." In Communications in Computer and Information Science, 101–10. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3005-5_9.
Повний текст джерелаTu, Juanjuan, Zongliang Gan, and Feng Liu. "Retinex Based Flicker-Free Low-Light Video Enhancement." In Pattern Recognition and Computer Vision, 367–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_31.
Повний текст джерелаNie, Xixi, Zilong Song, Bing Zhou, and Yating Wei. "LESN: Low-Light Image Enhancement via Siamese Network." In Lecture Notes in Electrical Engineering, 183–94. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6963-7_17.
Повний текст джерелаPanwar, Moomal, and Sanjay B. C. Gaur. "Inception-Based CNN for Low-Light Image Enhancement." In Computational Vision and Bio-Inspired Computing, 533–45. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9573-5_39.
Повний текст джерелаPriyadarshini, R., Arvind Bharani, E. Rahimankhan, and N. Rajendran. "Low-Light Image Enhancement Using Deep Convolutional Network." In Innovative Data Communication Technologies and Application, 695–705. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9651-3_57.
Повний текст джерелаТези доповідей конференцій з теми "Low-light enhancement and denoising"
Feng, Hansen, Lizhi Wang, Yuzhi Wang, and Hua Huang. "Learnability Enhancement for Low-light Raw Denoising." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548186.
Повний текст джерелаGuo, Yu, Yuxu Lu, Meifang Yang, and Ryan Wen Liu. "Low-light Image Enhancement with Deep Blind Denoising." In 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.
Повний текст джерелаGao, Yin, Chao Yan, Huixiong Zeng, Qiming Li, and Jun Li. "Adaptive Low-Light Image Enhancement with Decomposition Denoising." In 2022 7th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 2022. http://dx.doi.org/10.1109/icrae56463.2022.10056212.
Повний текст джерелаLim, Jaemoon, Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim. "Robust contrast enhancement of noisy low-light images: Denoising-enhancement-completion." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351583.
Повний текст джерелаSong, Yuda, Yunfang Zhu, and Xin Du. "Automatical Enhancement and Denoising of Extremely Low-light Images." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412195.
Повний текст джерелаWenshuai Yin, Xiangbo Lin, and Yi Sun. "A novel framework for low-light colour image enhancement and denoising." In 2011 3rd International Conference on Awareness Science and Technology (iCAST). IEEE, 2011. http://dx.doi.org/10.1109/icawst.2011.6163088.
Повний текст джерелаYu, Long, Haonan Su, and Cheolkon Jung. "Joint Enhancement And Denoising of Low Light Images Via JND Transform." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053027.
Повний текст джерелаWang, Jikun, Weixiang Liang, Xianbo Wang, and Zhixin Yang. "An Image Denoising and Enhancement Approach for Dynamic Low-light Environment." In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2022. http://dx.doi.org/10.1109/ipec54454.2022.9777630.
Повний текст джерелаZhou, Dewei, Zongxin Yang, and Yi Yang. "Pyramid Diffusion Models for Low-light Image Enhancement." In 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.
Повний текст джерелаLi, Lin, Ronggang Wang, Wenmin Wang, and Wen Gao. "A low-light image enhancement method for both denoising and contrast enlarging." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351501.
Повний текст джерелаЗвіти організацій з теми "Low-light enhancement and denoising"
Birkmire, Robert, Juejun Hu, and 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), May 2016. http://dx.doi.org/10.2172/1419008.
Повний текст джерела