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Noise synthesis is a challenging low-level vision task aiming to generate realistic noise given a clean image along with the camera settings.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Demystifying neural style transfer
Yanghao Li, Naiyan Wang, Jiaying Liu, and Xiaodi Hou · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
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A high-quality denoising dataset for smartphone cameras
Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Noise2noise: Learning image restoration without clean data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila · 2018
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Film grain synthesis for AV1 video codec
Andrey Norkin and Neil Birkbeck · 2018
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Noise flow: Noise modeling with conditional normalizing flows
Abdelrahman Abdelhamed, Marcus A Brubaker, and Michael S Brown · 2019
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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GLU variants improve transformer
Noam Shazeer · 2020
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Fastdvdnet: Towards real-time deep video denoising without flow estimation
Matias Tassano, Julie Delon, and Thomas Veit · 2020
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A physics-based noise formation model for extreme low-light raw denoising
Invertible denoising network: A light solution for real noise removal
Yang Liu, Zhenyue Qin, Saeed Anwar, Pan Ji, Dongwoo Kim, Sabrina Caldwell, and Tom Gedeon · 2021
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Rethinking noise synthesis and modeling in raw denoising
Yi Zhang, Hongwei Qin, Xiaogang Wang, and Hongsheng Li · 2021
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Deep-based film grain removal and synthesis
Zoubida Ameur, Wassim Hamidouche, Edouard François, Miloš Radosavljević, Daniel Menard, and Claire-Hélène Demarty · 2022
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Simple baselines for image restoration
Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun · 2022
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Modeling sRGB camera noise with normalizing flows
Shayan Kousha, Ali Maleky, Michael S Brown, and Marcus A Brubaker · 2022
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Kaixuan Wei, Ying Fu, Jiaolong Yang, and Hua Huang · 2020
Cited alongside, same era.
Dual adversarial network: Toward real-world noise removal and noise generation
Zongsheng Yue, Qian Zhao, Lei Zhang, and Deyu Meng · 2020
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Learning to generate realistic noisy images via pixel-level noise-aware adversarial training
Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, and Donglai Wei · 2021
Cited alongside, same era.
C2N: Practical generative noise modeling for real-world denoising
Geonwoon Jang, Wooseok Lee, Sanghyun Son, and Kyoung Mu Lee · 2021
Cited alongside, same era.
SwinIR: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
Cited alongside, same era.
Noise2noiseflow: Realistic camera noise modeling without clean images
Ali Maleky, Shayan Kousha, Michael S Brown, and Marcus A Brubaker · 2022
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Dancing under the stars: video denoising in starlight
Kristina Monakhova, Stephan R Richter, Laura Waller, and Vladlen Koltun · 2022
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Restormer: Efficient transformer for high-resolution image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2022
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Estimating fine-grained noise model via contrastive learning
Yunhao Zou and Ying Fu · 2022
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srgb real noise synthesizing with neighboring correlation-aware noise model
Zixuan Fu, Lanqing Guo, and Bihan Wen · 2023
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