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archive:appds:bibliograthy [05/07/2024 22:03] – ↷ Page moved from grants:archive:appds:bibliograthy to archive:appds:bibliograthy adminarchive:appds:bibliograthy [05/07/2024 22:19] (current) – ↷ Links adapted because of a move operation admin
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 ===== ANN and noise reduction ===== ===== ANN and noise reduction =====
  
-  * {{ grants:archive:appds:gutta_kadimesetty_ieeetrpms_2019.pdf |Kadimesetty 2019}} Convolutional Neural Network-Based Robust Denoising of Low-Dose Computed Tomography Perfusion Maps. Venkata S. Kadimesetty,  Sreedevi Gutta, Sriram Ganapathy, and Phaneendra K. Yalavarthy. +  * {{ archive:appds:gutta_kadimesetty_ieeetrpms_2019.pdf |Kadimesetty 2019}} Convolutional Neural Network-Based Robust Denoising of Low-Dose Computed Tomography Perfusion Maps. Venkata S. Kadimesetty,  Sreedevi Gutta, Sriram Ganapathy, and Phaneendra K. Yalavarthy. 
   * [[https://arxiv.org/pdf/1912.13171.pdf|ArXiv:1912.13171]] Deep Learning on Image Denoising: An Overview. Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin   * [[https://arxiv.org/pdf/1912.13171.pdf|ArXiv:1912.13171]] Deep Learning on Image Denoising: An Overview. Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin
   * [[https://arxiv.org/pdf/1910.09435.pdf|ArXiv:1910.09435]] Background  Rejection  in  Atmospheric  CherenkovTelescopes  using  Recurrent  Convolutional  NeuralNetworks. R.D. Parsons, S. Ohm   * [[https://arxiv.org/pdf/1910.09435.pdf|ArXiv:1910.09435]] Background  Rejection  in  Atmospheric  CherenkovTelescopes  using  Recurrent  Convolutional  NeuralNetworks. R.D. Parsons, S. Ohm
-  * arXiv: 1708.00961 {{ grants:archive:appds:1708.00961.pdf |Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss}} +  * arXiv: 1708.00961 {{ archive:appds:1708.00961.pdf |Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss}} 
-  * arXiv: 1807:08176 {{ grants:archive:appds:1807.08176-cnn.pdf |A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise}} +  * arXiv: 1807:08176 {{ archive:appds:1807.08176-cnn.pdf |A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise}} 
-  * Jingwen Chen and et. al. {{ grants:archive:appds:chen_image_blind_denoising_cvpr_2018_paper.pdf |Image Blind Denoising With Generative Adversarial Network Based Noise Modeling}} +  * Jingwen Chen and et. al. {{ archive:appds:chen_image_blind_denoising_cvpr_2018_paper.pdf |Image Blind Denoising With Generative Adversarial Network Based Noise Modeling}} 
-  * Yasushi Amari and et. al. {{ grants:archive:appds:impuls_noise_reduction.pdf |A Study on Impulse Noise Reduction Using CNN Learned by Divided Images}} +  * Yasushi Amari and et. al. {{ archive:appds:impuls_noise_reduction.pdf |A Study on Impulse Noise Reduction Using CNN Learned by Divided Images}} 
-  * ERIC KVIST {{ grants:archive:appds:mlp_vs_cnn.pdf |A comparative study between MLP and CNN for noise reduction on images}} +  * ERIC KVIST {{ archive:appds:mlp_vs_cnn.pdf |A comparative study between MLP and CNN for noise reduction on images}} 
-  * Kartik Audhkhasi, Osonde Osoba, Bart Kosko, {{ grants:archive:appds:n-cnn-published-may-2016.pdf |Noise-enhanced convolutional neural networks}}, Neural Networks 78 (2016) 15–23 +  * Kartik Audhkhasi, Osonde Osoba, Bart Kosko, {{ archive:appds:n-cnn-published-may-2016.pdf |Noise-enhanced convolutional neural networks}}, Neural Networks 78 (2016) 15–23 
-  * Fabian Dietrichson {{ grants:archive:appds:ius_2018_proceedings_ultrasound_speckle_reduction_using_generative_adversial_networks.pdf |Ultrasound speckle reduction using generative adversial networks}} +  * Fabian Dietrichson {{ archive:appds:ius_2018_proceedings_ultrasound_speckle_reduction_using_generative_adversial_networks.pdf |Ultrasound speckle reduction using generative adversial networks}} 
-  * C. J. Díaz Baso, J. de la Cruz Rodríguez, and S. Danilovic, {{ grants:archive:appds:1908.02815-solar_denoising.pdf |Solar image denoising with convolutional neural networks}}. ArXiv:1908.02815. +  * C. J. Díaz Baso, J. de la Cruz Rodríguez, and S. Danilovic, {{ archive:appds:1908.02815-solar_denoising.pdf |Solar image denoising with convolutional neural networks}}. ArXiv:1908.02815. 
-  * Kai Yi, Yi Guo, Yanan Fan, Jan Hamann, Yu Guang Wang, {{ grants:archive:appds:2001.11651-cosmo_vae.pdf |CosmoVAE: Variational Autoencoder for CMB Image Inpainting}}. ArXiv:2001.11651. +  * Kai Yi, Yi Guo, Yanan Fan, Jan Hamann, Yu Guang Wang, {{ archive:appds:2001.11651-cosmo_vae.pdf |CosmoVAE: Variational Autoencoder for CMB Image Inpainting}}. ArXiv:2001.11651. 
-  * Rich Ormiston, et. al. {{ grants:archive:appds:2005.06534-gwave.pdf |Noise Reduction in Gravitational-wave Data via Deep Learning}}. ArXiv:2005.06534. +  * Rich Ormiston, et. al. {{ archive:appds:2005.06534-gwave.pdf |Noise Reduction in Gravitational-wave Data via Deep Learning}}. ArXiv:2005.06534. 
-  * Masato Shirasaki, et. al. {{ grants:archive:appds:1911.12890-gan.pdf |Decoding Cosmological Information in Weak-Lensing Mass Maps with Generative Adversarial Networks}}. ArXiv:1911.12890. +  * Masato Shirasaki, et. al. {{ archive:appds:1911.12890-gan.pdf |Decoding Cosmological Information in Weak-Lensing Mass Maps with Generative Adversarial Networks}}. ArXiv:1911.12890. 
-  * Aryeh Brill, et. al. {{ grants:archive:appds:2001.03602-multiple_iact.pdf |Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes}}. ArXiv:2001.03602.+  * Aryeh Brill, et. al. {{ archive:appds:2001.03602-multiple_iact.pdf |Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes}}. ArXiv:2001.03602.
  
  
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   * [[https://taiga-experiment.info/taiga-iact|Сайт проекта TAIGA/TUNKA]]   * [[https://taiga-experiment.info/taiga-iact|Сайт проекта TAIGA/TUNKA]]
   * [[http://x4u.lebedev.ru/che2019/f/talks/02.Kuzmichev.pdf|Экспериментальный комплекс TAIGA: статус, результаты, перспективы]].   * [[http://x4u.lebedev.ru/che2019/f/talks/02.Kuzmichev.pdf|Экспериментальный комплекс TAIGA: статус, результаты, перспективы]].
-  * {{ grants:archive:appds:the_future_of_gamma-ray_astronomy.pdf |The future of gamma-ray astronomy}} +  * {{ archive:appds:the_future_of_gamma-ray_astronomy.pdf |The future of gamma-ray astronomy}} 
-  * {{ grants:archive:appds:mephi_lecture2_gamma_rays.pdf |Gamma-ray Asrophysics}}+  * {{ archive:appds:mephi_lecture2_gamma_rays.pdf |Gamma-ray Asrophysics}}
   * [[https://indico.cern.ch/event/568904/contributions/2646096/attachments/1486401/2308455/ISAPP_Lecture_I.pdf|Gamma-ray astronomyLecture I: History, instruments & detection methods]] Markus Ackermann   * [[https://indico.cern.ch/event/568904/contributions/2646096/attachments/1486401/2308455/ISAPP_Lecture_I.pdf|Gamma-ray astronomyLecture I: History, instruments & detection methods]] Markus Ackermann
   * [[https://arxiv.org/pdf/1508.05190.pdf|Space- and Ground-BasedGamma-Ray Astrophysics]]. Stefan Funk   * [[https://arxiv.org/pdf/1508.05190.pdf|Space- and Ground-BasedGamma-Ray Astrophysics]]. Stefan Funk
archive/appds/bibliograthy.1720206216.txt.gz · Last modified: 05/07/2024 22:03 by admin