Artículos de revistas sobre el tema "Denoisers"
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Gu, Jeongmin, Jose A. Iglesias-Guitian y Bochang Moon. "Neural James-Stein Combiner for Unbiased and Biased Renderings". ACM Transactions on Graphics 41, n.º 6 (30 de noviembre de 2022): 1–14. http://dx.doi.org/10.1145/3550454.3555496.
Texto completoZheng, Shaokun, Fengshi Zheng, Kun Xu y Ling-Qi Yan. "Ensemble denoising for Monte Carlo renderings". ACM Transactions on Graphics 40, n.º 6 (diciembre de 2021): 1–17. http://dx.doi.org/10.1145/3478513.3480510.
Texto completoHofmann, Nikolai, Jon Hasselgren y Jacob Munkberg. "Joint Neural Denoising of Surfaces and Volumes". Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, n.º 1 (12 de mayo de 2023): 1–16. http://dx.doi.org/10.1145/3585497.
Texto completoHan, Kyu Beom, Olivia G. Odenthal, Woo Jae Kim y Sung-Eui Yoon. "Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising". Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, n.º 1 (12 de mayo de 2023): 1–19. http://dx.doi.org/10.1145/3585505.
Texto completoLiu, Shuaiqi, Tong Liu, Lele Gao, Hailiang Li, Qi Hu, Jie Zhao y Chong Wang. "Convolutional Neural Network and Guided Filtering for SAR Image Denoising". Remote Sensing 11, n.º 6 (23 de marzo de 2019): 702. http://dx.doi.org/10.3390/rs11060702.
Texto completoChoi, Joon Hee, Omar A. Elgendy y Stanley H. Chan. "Optimal Combination of Image Denoisers". IEEE Transactions on Image Processing 28, n.º 8 (agosto de 2019): 4016–31. http://dx.doi.org/10.1109/tip.2019.2903321.
Texto completoMeng, Xiyan y Fang Zhuang. "A New Boosting Algorithm for Shrinkage Curve Learning". Mathematical Problems in Engineering 2022 (15 de abril de 2022): 1–14. http://dx.doi.org/10.1155/2022/6339758.
Texto completoLiu, Yukun, Bowen Wan, Daming Shi y Xiaochun Cheng. "Generative Recorrupted-to-Recorrupted: An Unsupervised Image Denoising Network for Arbitrary Noise Distribution". Remote Sensing 15, n.º 2 (6 de enero de 2023): 364. http://dx.doi.org/10.3390/rs15020364.
Texto completoGalande, Ashwini S., Vikas Thapa, Hanu Phani Ram Gurram y Renu John. "Untrained deep network powered with explicit denoiser for phase recovery in inline holography". Applied Physics Letters 122, n.º 13 (27 de marzo de 2023): 133701. http://dx.doi.org/10.1063/5.0144795.
Texto completoKim, Bong-Hyun y S. Madhavi. "Method for Quantum Denoisers Using Convolutional Neural Network". Computational Intelligence and Neuroscience 2022 (6 de octubre de 2022): 1–7. http://dx.doi.org/10.1155/2022/4885897.
Texto completoGavaskar, Ruturaj G., Chirayu D. Athalye y Kunal N. Chaudhury. "On Plug-and-Play Regularization Using Linear Denoisers". IEEE Transactions on Image Processing 30 (2021): 4802–13. http://dx.doi.org/10.1109/tip.2021.3075092.
Texto completoZhang, Jie, Qiyuan Zhang, Xixuan Zhao y Jiangming Kan. "Boosting denoisers with reinforcement learning for image restoration". Soft Computing 26, n.º 7 (20 de febrero de 2022): 3261–72. http://dx.doi.org/10.1007/s00500-022-06840-3.
Texto completoGavaskar, Ruturaj G. y Kunal N. Chaudhury. "Plug-and-Play ISTA Converges With Kernel Denoisers". IEEE Signal Processing Letters 27 (2020): 610–14. http://dx.doi.org/10.1109/lsp.2020.2986643.
Texto completoZhou, Yuqian, Jianbo Jiao, Haibin Huang, Yang Wang, Jue Wang, Honghui Shi y Thomas Huang. "When AWGN-Based Denoiser Meets Real Noises". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 13074–81. http://dx.doi.org/10.1609/aaai.v34i07.7009.
Texto completoYu, Lijia, Jie Luo, Shaoping Xu, Xiaojun Chen y Nan Xiao. "An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers". Applied Sciences 12, n.º 12 (19 de junio de 2022): 6227. http://dx.doi.org/10.3390/app12126227.
Texto completoJoo, Sunghwan, Sungmin Cha y Taesup Moon. "DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 4031–38. http://dx.doi.org/10.1609/aaai.v33i01.33014031.
Texto completoXu, Xiaojian, Yu Sun, Jiaming Liu, Brendt Wohlberg y Ulugbek S. Kamilov. "Provable Convergence of Plug-and-Play Priors With MMSE Denoisers". IEEE Signal Processing Letters 27 (2020): 1280–84. http://dx.doi.org/10.1109/lsp.2020.3006390.
Texto completoHait-Fraenkel, Ester y Guy Gilboa. "Revealing stable and unstable modes of denoisers through nonlinear eigenvalue analysis". Journal of Visual Communication and Image Representation 75 (febrero de 2021): 103041. http://dx.doi.org/10.1016/j.jvcir.2021.103041.
Texto completoCascarano, Pasquale, Elena Loli Piccolomini, Elena Morotti y Andrea Sebastiani. "Plug-and-Play gradient-based denoisers applied to CT image enhancement". Applied Mathematics and Computation 422 (junio de 2022): 126967. http://dx.doi.org/10.1016/j.amc.2022.126967.
Texto completoLiu, Yiwen, Shaoping Xu y Zhenyu Lin. "An Improved Combination of Image Denoisers Using Spatial Local Fusion Strategy". IEEE Access 8 (2020): 150407–21. http://dx.doi.org/10.1109/access.2020.3016766.
Texto completoGross, Dennis, Christoph Schmidl, Nils Jansen y Guillermo A. Pérez. "Model Checking for Adversarial Multi-Agent Reinforcement Learning with Reactive Defense Methods". Proceedings of the International Conference on Automated Planning and Scheduling 33, n.º 1 (1 de julio de 2023): 162–70. http://dx.doi.org/10.1609/icaps.v33i1.27191.
Texto completoNearing, Jacob T., Gavin M. Douglas, André M. Comeau y Morgan G. I. Langille. "Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches". PeerJ 6 (8 de agosto de 2018): e5364. http://dx.doi.org/10.7717/peerj.5364.
Texto completoThomas, Manu Mathew, Gabor Liktor, Christoph Peters, Sungye Kim, Karthik Vaidyanathan y Angus G. Forbes. "Temporally Stable Real-Time Joint Neural Denoising and Supersampling". Proceedings of the ACM on Computer Graphics and Interactive Techniques 5, n.º 3 (25 de julio de 2022): 1–22. http://dx.doi.org/10.1145/3543870.
Texto completoWu, Huixuan, Pan Du, Rohan Kokate y Jian-Xun Wang. "A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking". PLOS ONE 16, n.º 7 (9 de julio de 2021): e0254051. http://dx.doi.org/10.1371/journal.pone.0254051.
Texto completoDeledalle, Charles-Alban, Loic Denis, Sonia Tabti y Florence Tupin. "MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?" IEEE Transactions on Image Processing 26, n.º 9 (septiembre de 2017): 4389–403. http://dx.doi.org/10.1109/tip.2017.2713946.
Texto completoAhmad, Rizwan, Charles A. Bouman, Gregery T. Buzzard, Stanley Chan, Sizhuo Liu, Edward T. Reehorst y Philip Schniter. "Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery". IEEE Signal Processing Magazine 37, n.º 1 (enero de 2020): 105–16. http://dx.doi.org/10.1109/msp.2019.2949470.
Texto completoLi, Zun y Jin Wu. "Learning Deep CNN Denoiser Priors for Depth Image Inpainting". Applied Sciences 9, n.º 6 (15 de marzo de 2019): 1103. http://dx.doi.org/10.3390/app9061103.
Texto completoMa, Ruijun, Shuyi Li, Bob Zhang y Zhengming Li. "Generative Adaptive Convolutions for Real-World Noisy Image Denoising". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 2 (28 de junio de 2022): 1935–43. http://dx.doi.org/10.1609/aaai.v36i2.20088.
Texto completoZhang, Hao, Xiuyan Yang y Jianwei Ma. "Can learning from natural image denoising be used for seismic data interpolation?" GEOPHYSICS 85, n.º 4 (7 de mayo de 2020): WA115—WA136. http://dx.doi.org/10.1190/geo2019-0243.1.
Texto completoDing, Yuehao, Hao Wu y Guowu Yuan. "A two-stage modular blind denoising algorithm based on real scene". Journal of Physics: Conference Series 2216, n.º 1 (1 de marzo de 2022): 012071. http://dx.doi.org/10.1088/1742-6596/2216/1/012071.
Texto completoMa, Yanting, Cynthia Rush y Dror Baron. "Analysis of Approximate Message Passing With Non-Separable Denoisers and Markov Random Field Priors". IEEE Transactions on Information Theory 65, n.º 11 (noviembre de 2019): 7367–89. http://dx.doi.org/10.1109/tit.2019.2934152.
Texto completoLiehr, Sascha, Christopher Borchardt y Sven Münzenberger. "Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers". Optics Express 28, n.º 26 (14 de diciembre de 2020): 39311. http://dx.doi.org/10.1364/oe.402789.
Texto completoKim, Kwanyoung, Shakarim Soltanayev y Se Young Chun. "Unsupervised Training of Denoisers for Low-Dose CT Reconstruction Without Full-Dose Ground Truth". IEEE Journal of Selected Topics in Signal Processing 14, n.º 6 (octubre de 2020): 1112–25. http://dx.doi.org/10.1109/jstsp.2020.3007326.
Texto completoDeng, Xi, Miloš Hašan, Nathan Carr, Zexiang Xu y Steve Marschner. "Path graphs". ACM Transactions on Graphics 40, n.º 6 (diciembre de 2021): 1–15. http://dx.doi.org/10.1145/3478513.3480547.
Texto completode Santi, Natalí S. M. y L. Raul Abramo. "Improving cosmological covariance matrices with machine learning". Journal of Cosmology and Astroparticle Physics 2022, n.º 09 (1 de septiembre de 2022): 013. http://dx.doi.org/10.1088/1475-7516/2022/09/013.
Texto completoHe, Yilin, Yunhua Yao, Yu He, Zhengqi Huang, Pengpeng Ding, Dalong Qi, Zhiyong Wang, Tianqing Jia, Zhenrong Sun y Shian Zhang. "High-speed compressive wide-field fluorescence microscopy with an alternant deep denoisers-based image reconstruction algorithm". Optics and Lasers in Engineering 165 (junio de 2023): 107541. http://dx.doi.org/10.1016/j.optlaseng.2023.107541.
Texto completoBen, Guangli, Xifeng Zheng, Yongcheng Wang, Ning Zhang y Xin Zhang. "A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal". Applied Sciences 11, n.º 2 (12 de enero de 2021): 673. http://dx.doi.org/10.3390/app11020673.
Texto completoLin, Huangxing, Yihong Zhuang, Xinghao Ding, Delu Zeng, Yue Huang, Xiaotong Tu y John Paisley. "Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 2 (26 de junio de 2023): 1586–94. http://dx.doi.org/10.1609/aaai.v37i2.25245.
Texto completoZhuang, Lina, Michael K. Ng y Xiyou Fu. "Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior". Remote Sensing 13, n.º 20 (13 de octubre de 2021): 4098. http://dx.doi.org/10.3390/rs13204098.
Texto completoZhuang, Lina, Michael K. Ng y Xiyou Fu. "Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior". Remote Sensing 13, n.º 20 (13 de octubre de 2021): 4098. http://dx.doi.org/10.3390/rs13204098.
Texto completoHuang, Zhenghua, Zifan Zhu, Yaozong Zhang, Zhicheng Wang, Biyun Xu, Jun Liu, Shaoyi Li y Hao Fang. "MD3: Model-Driven Deep Remotely Sensed Image Denoising". Remote Sensing 15, n.º 2 (11 de enero de 2023): 445. http://dx.doi.org/10.3390/rs15020445.
Texto completoDabbech, A., M. Terris, A. Jackson, M. Ramatsoku, O. M. Smirnov y Y. Wiaux. "First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006". Astrophysical Journal Letters 939, n.º 1 (26 de octubre de 2022): L4. http://dx.doi.org/10.3847/2041-8213/ac98af.
Texto completoZhao, Shengrong y Hu Liang. "Multi-frame super resolution via deep plug-and-play CNN regularization". Journal of Inverse and Ill-posed Problems 28, n.º 4 (1 de agosto de 2020): 533–55. http://dx.doi.org/10.1515/jiip-2019-0054.
Texto completoMc Grath, Orlaith, Mohammad W. Sarfraz, Abha Gupta, Yan Yang y Tariq Aslam. "Clinical Utility of Artificial Intelligence Algorithms to Enhance Wide-Field Optical Coherence Tomography Angiography Images". Journal of Imaging 7, n.º 2 (10 de febrero de 2021): 32. http://dx.doi.org/10.3390/jimaging7020032.
Texto completoZhao, Zitian, Wenhan Zhan, Yamin Cheng, Hancong Duan, Yue Wu y Ke Zhang. "Denoising by Decorated Noise: An Interpretability-Based Framework for Adversarial Example Detection". Wireless Communications and Mobile Computing 2023 (11 de abril de 2023): 1–11. http://dx.doi.org/10.1155/2023/7669696.
Texto completoOh, Geunwoo, Jonghee Back, Jae-Pil Heo y Bochang Moon. "Robust Image Denoising of No-Flash Images Guided by Consistent Flash Images". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 2 (26 de junio de 2023): 1993–2001. http://dx.doi.org/10.1609/aaai.v37i2.25291.
Texto completoSang, De Yi, Jian Jun Zhao y Li Bin Yang. "Denoising Method for Calibration Data of Landing Guidance Radar Based on EMD and Wavelet". Advanced Materials Research 962-965 (junio de 2014): 2856–62. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2856.
Texto completoKhan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman y Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising". Sensors 21, n.º 9 (24 de abril de 2021): 2998. http://dx.doi.org/10.3390/s21092998.
Texto completoZou, XiuFang, Dingju Zhu, Jun Huang, Wei Lu, Xinchu Yao y Zhaotong Lian. "WGAN-Based Image Denoising Algorithm". Journal of Global Information Management 30, n.º 9 (enero de 2022): 1–20. http://dx.doi.org/10.4018/jgim.300821.
Texto completoSu, Yunhao, Caiwen Ma, Junfeng Han, Xuan Wang, Yuanyuan Wang y Zhou Ji. "Research on Magnetohydrodynamic Angular Rate Sensor Denoising for a Space Laser Stabilization Control System". Applied Sciences 13, n.º 10 (10 de mayo de 2023): 5895. http://dx.doi.org/10.3390/app13105895.
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