Literatura académica sobre el tema "Color denoising"
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Artículos de revistas sobre el tema "Color denoising"
Netravali, Ilka A., Robert J. Holt y Charles Webb. "Perceptual denoising of color images". International Journal of Imaging Systems and Technology 20, n.º 3 (16 de agosto de 2010): 215–22. http://dx.doi.org/10.1002/ima.20240.
Texto completoKomatsu, Rina y Tad Gonsalves. "Comparing U-Net Based Models for Denoising Color Images". AI 1, n.º 4 (12 de octubre de 2020): 465–87. http://dx.doi.org/10.3390/ai1040029.
Texto completoThomas, Jency y Remya S. "PLOW Filter for Color Image Denoising". International Journal of Computer Applications 79, n.º 13 (18 de octubre de 2013): 1–7. http://dx.doi.org/10.5120/13798-1855.
Texto completoShen, Yi, Bin Han y Elena Braverman. "Adaptive frame-based color image denoising". Applied and Computational Harmonic Analysis 41, n.º 1 (julio de 2016): 54–74. http://dx.doi.org/10.1016/j.acha.2015.04.001.
Texto completoLukac, Rastislav, Konstantinos N. Plataniotis y Anastasios N. Venetsanopoulos. "Color image denoising using evolutionary computation". International Journal of Imaging Systems and Technology 15, n.º 5 (2005): 236–51. http://dx.doi.org/10.1002/ima.20058.
Texto completoHe, Shui Ming y Xue Lin Li. "Applications of Color Morphology in Image Denoising". Advanced Materials Research 1037 (octubre de 2014): 393–97. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.393.
Texto completoLiang, Dong Tai. "Color Image Denoising Using Gaussian Multiscale Multivariate Image Analysis". Applied Mechanics and Materials 37-38 (noviembre de 2010): 248–52. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.248.
Texto completoPark, Yunjin, Sukho Lee, Byeongseon Jeong y Jungho Yoon. "Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network". Sensors 20, n.º 10 (24 de mayo de 2020): 2970. http://dx.doi.org/10.3390/s20102970.
Texto completoHan, Zhenghao, Li Li, Weiqi Jin, Xia Wang, Gangcheng Jiao, Xuan Liu y Hailin Wang. "Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS". Sensors 21, n.º 11 (4 de junio de 2021): 3891. http://dx.doi.org/10.3390/s21113891.
Texto completoShamshad, Fahad, M. Mohsin Riaz y Abdul Ghafoor. "Poisson Denoising for Astronomical Images". Advances in Astronomy 2018 (10 de junio de 2018): 1–7. http://dx.doi.org/10.1155/2018/2417939.
Texto completoTesis sobre el tema "Color denoising"
Rafi, Nazari Mina. "Denoising and Demosaicking of Color Images". Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35802.
Texto completoDeng, Hao. "Mathematical approaches to digital color image denoising". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31708.
Texto completoCommittee Chair: Haomin Zhou; Committee Member: Luca Dieci; Committee Member: Ronghua Pan; Committee Member: Sung Ha Kang; Committee Member: Yang Wang. Part of the SMARTech Electronic Thesis and Dissertation Collection.
IRFAN, MUHAMMAD ABEER. "Joint geometry and color denoising for 3D point clouds". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2912976.
Texto completoZhang, Chen. "Poisson Noise Parameter Estimation and Color Image Denoising for Real Camera Hardware". University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1575968356242716.
Texto completoÅström, Freddie, George Baravdish y Michael Felsberg. "On Tensor-Based PDEs and their Corresponding Variational Formulations with Application to Color Image Denoising". Linköpings universitet, Datorseende, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79603.
Texto completoNACIP
GARNICS
ELLIIT
Malek, Mohamed. "Extension de l'analyse multi-résolution aux images couleurs par transformées sur graphes". Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2304/document.
Texto completoIn our work, we studied the extension of the multi-resolution analysis for color images by using transforms on graphs. In this context, we deployed three different strategies of analysis. Our first approach consists of computing the graph of an image using the psychovisual information and analyzing it by using the spectral graph wavelet transform. We thus have defined a wavelet transform based on a graph with perceptual information by using the CIELab color distance. Results in image restoration highlight the interest of the appropriate use of color information. In the second strategy, we propose a novel recovery algorithm for image inpainting represented in the graph domain. Motivated by the efficiency of the wavelet regularization schemes and the success of the nonlocal means methods we construct an algorithm based on the recovery of information in the graph wavelet domain. At each step the damaged structure are estimated by computing the non local graph then we apply the graph wavelet regularization model using the SGWT coefficient. The results are very encouraging and highlight the use of the perceptual informations. In the last strategy, we propose a new approach of decomposition for signals defined on a complete graphs. This method is based on the exploitation of of the laplacian matrix proprieties of the complete graph. In the context of image processing, the use of the color distance is essential to identify the specificities of the color image. This approach opens new perspectives for an in-depth study of its behavior
Tang, Hsueh-Yung y 唐學用. "Color Filter Array Denoising Method for Digital Cameras". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/8pv2r8.
Texto completo國立交通大學
電機學院碩士在職專班電機與控制組
96
Nowadays, traditional film camera has almost been replaced by digital camera in commercial market. This trend is not only on camera, but also on any product in which the signal can be digitalized, since digital information would be much convenience to be processed, stored, and transmitted. Image processing in digital camera consists of many processes. Almost all of the processes would enhance noise added in images. The best way to make noise strength be minimized is to reduce noise in front of any image processing. The difficulty of image denoising is always to preserve edge information, and filters out noise in flat area simultaneously. In this paper, we have presented a denoising method which consists of three ideas. One is to filter noisy pixel based on nearest pattern to keep edge information, another one is to use camera noise characteristic to judge the uniformity of current processed area and the last one is to make use of the property of spatial masking to keep edge information again on the highly texture area.
Huang, Yi-Sheng y 黃弌聖. "Color Image Denoising via Sparse and Redundant Representations over Online Dictionary". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/54706257264214133641.
Texto completo國立中正大學
資訊工程研究所
99
An image is easy to get noise by transferring, so image denoising is to address this problem. Image denoising is an ill-posed problem in image processing. In this study, a color image denoising method using sparse and redundant representations over online dictionary is proposed. The proposed image denoising method contains six stages: (1) separating the noisy color image to the RGB components; (2) color decorrelation; (3) Constructing a dictionary which name called “online dictionary” by online dictionary learning algorithm with two images. (4) denoising the three components by online dictionary separately; (5) color correlation; and (6) merging the denoised RGB components to a denoisied color image. Based on experimental results obtained in this study, the subjective or objective measure for the results, the proposed method provides the better image denoising results compared with two comparison methods.
Yoon, Miun. "Variational and partial differential equation models for color image denoising and their numerical approximations using finite element methods". 2006. http://etd.utk.edu/2006/YoonMiun.pdf.
Texto completo(6905153), Omar A. Elgendy. "Image Processing for Quanta Image Sensors". Thesis, 2019.
Buscar texto completoCapítulos de libros sobre el tema "Color denoising"
Zhang, Jian-jun, Jian-li Zhang y Meng Gao. "Two Effective Algorithms for Color Image Denoising". En Lecture Notes in Computer Science, 207–17. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68345-4_19.
Texto completoLukin, Vladimir, Sergey Abramov, Ruslan Kozhemiakin, Alexey Rubel, Mikhail Uss, Nikolay Ponomarenko, Victoriya Abramova et al. "DCT-Based Color Image Denoising: Efficiency Analysis and Prediction". En Color Image and Video Enhancement, 55–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09363-5_3.
Texto completoBosco, Angelo, Sebastiano Battiato, Arcangelo Bruna y Rosetta Rizzo. "Texture Sensitive Denoising for Single Sensor Color Imaging Devices". En Lecture Notes in Computer Science, 130–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03265-3_14.
Texto completoSoulard, Raphaël y Philippe Carré. "Colour Extension of Monogenic Wavelets with Geometric Algebra: Application to Color Image Denoising". En Quaternion and Clifford Fourier Transforms and Wavelets, 247–68. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0603-9_12.
Texto completoShyjila, P. A. y M. Wilscy. "Non Local Means Image Denoising for Color Images Using PCA". En Advances in Computer Science and Information Technology, 288–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17857-3_29.
Texto completoPark, Hyun y Young Shik Moon. "Automatic Denoising of 2D Color Face Images Using Recursive PCA Reconstruction". En Advanced Concepts for Intelligent Vision Systems, 799–809. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11864349_73.
Texto completoMoreno, Rodrigo, Miguel Angel Garcia, Domenec Puig y Carme Julià. "On Adapting the Tensor Voting Framework to Robust Color Image Denoising". En Computer Analysis of Images and Patterns, 492–500. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_60.
Texto completoGoossens, Bart, Hiêp Luong, Jan Aelterman, Aleksandra Pižurica y Wilfried Philips. "A GPU-Accelerated Real-Time NLMeans Algorithm for Denoising Color Video Sequences". En Advanced Concepts for Intelligent Vision Systems, 46–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17691-3_5.
Texto completoMújica-Vargas, Dante, Arturo Rendón-Castro, Manuel Matuz-Cruz y Christian Garcia-Aquino. "Multi-core Median Redescending M-Estimator for Impulsive Denoising in Color Images". En Lecture Notes in Computer Science, 261–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77004-4_25.
Texto completoÅström, Freddie, George Baravdish y Michael Felsberg. "On Tensor-Based PDEs and Their Corresponding Variational Formulations with Application to Color Image Denoising". En Computer Vision – ECCV 2012, 215–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33712-3_16.
Texto completoActas de conferencias sobre el tema "Color denoising"
Rabie, T. "Robust Color Video Denoising". En IEEE International Conference on Computer Systems and Applications, 2006. IEEE, 2006. http://dx.doi.org/10.1109/aiccsa.2006.205180.
Texto completoHuang, Xinjian, Bo Du y Weiwei Liu. "Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/89.
Texto completoSaito, Takahiro, Nobuhiro Fujii y Takashi Komatsu. "Iterative soft color-shrinkage for color-image denoising". En 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414246.
Texto completoThomas, B. A. y J. J. Rodriguez. "Wavelet-based color image denoising". En Proceedings of 7th IEEE International Conference on Image Processing. IEEE, 2000. http://dx.doi.org/10.1109/icip.2000.899831.
Texto completoDai, Jingjing, Oscar C. Au, Wen Yang, Chao Pang, Feng Zou y Xing Wen. "Color video denoising based on adaptive color space conversion". En 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5538013.
Texto completoMeher, Sukadev. "Color Image Denoising with Multi-channel Spatial Color Filtering". En 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.60.
Texto completoBettahar, S., P. Lambert y A. Boudghene Stambouli. "Anisotropic color image denoising and sharpening". En 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025540.
Texto completoJoshi, N., C. L. Zitnick, R. Szeliski y D. J. Kriegman. "Image deblurring and denoising using color priors". En 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206802.
Texto completoTeimouri, Mehdi, Ehsan Vahedi, Alireza Nasiri Avanaki y Zabihollah Hasan Shahi. "An efficient denoising method for color images". En 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA). IEEE, 2007. http://dx.doi.org/10.1109/isspa.2007.4555310.
Texto completoJoshi, Neel, C. Lawrence Zitnick, Richard Szeliski y David J. Kriegman. "Image deblurring and denoising using color priors". En 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206802.
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