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Deep denoising models require extensive real-world training data, which is challenging to acquire.
Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data
Foi, A.; Trimeche, M.; Katkovnik, V.; and Egiazarian, K. 2008 · 2008
Earlier work this paper cites.
Clipped noisy images: Heteroskedastic modeling and practical denoising
Foi, A. 2009 · 2009
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Denoising diffusion implicit models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2010
Earlier work this paper cites.
A holistic approach to cross-channel image noise modeling and its application to image denoising
Nam, S.; Hwang, Y.; Matsushita, Y.; and Kim, S. J. 2016 · 2016
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2017 · 2017
Earlier work this paper cites.
Benchmarking denoising algorithms with real photographs
Plotz, T.; and Roth, S. 2017 · 2017
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K.; Zuo, W.; Chen, Y.; Meng, D.; and Zhang, L. 2017 · 2017
Earlier work this paper cites.
A high-quality denoising dataset for smartphone cameras
Abdelhamed, A.; Lin, S.; and Brown, M. S. 2018 · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2018 · 2018
Earlier work this paper cites.
Are gans created equal? a large-scale study
Lucic, M.; Kurach, K.; Michalski, M.; Gelly, S.; and Bousquet, O. 2018 · 2018
Earlier work this paper cites.
Which training methods for GANs do actually converge?
Mescheder, L.; Geiger, A.; and Nowozin, S. 2018 · 2018
Earlier work this paper cites.
Deep image prior
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2018 · 2018
Earlier work this paper cites.
Real-world noisy image denoising: A new benchmark
Xu, J.; Li, H.; Liang, Z.; Zhang, D.; and Zhang, L. 2018 · 2018
Cited alongside, same era.
FFDNet: Toward a fast and flexible solution for CNN-based image denoising
Zhang, K.; Zuo, W.; and Zhang, L. 2018 · 2018
Cited alongside, same era.
Noise flow: Noise modeling with conditional normalizing flows
Abdelhamed, A.; Brubaker, M. A.; and Brown, M. S. 2019 · 2019
Cited alongside, same era.
Unprocessing images for learned raw denoising
Brooks, T.; Mildenhall, B.; Xue, T.; Chen, J.; Sharlet, D.; and Barron, J. T. 2019 · 2019
Cited alongside, same era.
Toward convolutional blind denoising of real photographs
Guo, S.; Yan, Z.; Zhang, K.; Zuo, W.; and Zhang, L. 2019 · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Learning to generate realistic noisy images via pixel-level noise-aware adversarial training
Cai, Y.; Hu, X.; Wang, H.; Zhang, Y.; Pfister, H.; and Wei, D. 2021 · 2021
Later among the works it cites.
C2n: Practical generative noise modeling for real-world denoising
Jang, G.; Lee, W.; Son, S.; and Lee, K. M. 2021 · 2021
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Noise estimation for generative diffusion models
San-Roman, R.; Nachmani, E.; and Wolf, L. 2021 · 2021
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Multi-stage progressive image restoration
Zamir, S. W.; Arora, A.; Khan, S.; Hayat, M.; Khan, F. S.; Yang, M.-H.; and Shao, L. 2021 · 2021
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Cold diffusion: Inverting arbitrary image transforms without noise
Bansal, A.; Borgnia, E.; Chu, H.-M.; Li, J. S.; Kazemi, H.; Huang, F.; Goldblum, M.; Geiping, J.; and Goldstein, T. 2022 · 2022
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Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Cited alongside, same era.
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling
Kim, D.-W.; Chung, J. R.; and Jung, S.-W. 2019 · 2019
Cited alongside, same era.
Singan: Learning a generative model from a single natural image
Shaham, T. R.; Dekel, T.; and Michaeli, T. 2019 · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
Transfer learning from synthetic to real-noise denoising with adaptive instance normalization
Kim, Y.; Soh, J. W.; Park, G. Y.; and Cho, N. I. 2020 · 2020
Cited alongside, same era.
Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation
Yue, Z.; Zhao, Q.; Zhang, L.; and Meng, D. 2020 · 2020
Cited alongside, same era.
When awgn-based denoiser meets real noises
Zhou, Y.; Jiao, J.; Huang, H.; Wang, Y.; Wang, J.; Shi, H.; and Huang, T. 2020 · 2020
Cited alongside, same era.
Diffusiondet: Diffusion model for object detection
Chen, S.; Sun, P.; Song, Y.; and Luo, P. 2022 · 2022
Later among the works it cites.
Modeling sRGB Camera Noise with Normalizing Flows
Kousha, S.; Maleky, A.; Brown, M. S.; and Brubaker, M. A. 2022 · 2022
Later among the works it cites.
Uformer: A General U-Shaped Transformer for Image Restoration
Wang, Z.; Cun, X.; Bao, J.; Zhou, W.; Liu, J.; and Li, H. 2022 · 2022
Later among the works it cites.
Restormer: Efficient Transformer for High-Resolution Image Restoration
Zamir, S. W.; Arora, A.; Khan, S.; Hayat, M.; Khan, F. S.; and Yang, M.-H. 2022 · 2022
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sRGB Real Noise Synthesizing with Neighboring Correlation-Aware Noise Model
Fu, Z.; Guo, L.; and Wen, B. 2023 · 2023
Closest in time.
LSDIR Dataset: A Large Scale Dataset for Image Restoration
Li, Y.; Zhang, K.; Liang, J.; Cao, J.; Liu, C.; Gong, R.; Zhang, Y.; Tang, H.; Liu, Y.; Demandolx, D.; Ranjan, R.; Timofte, R.; and Van Gool, L. 2023 · 2023
Closest in time.
A Generative Model for Digital Camera Noise Synthesis
Song, M.; Zhang, Y.; Aydın, T. O.; Mansour, E. A.; and Schroers, C. 2023 · 2023
Closest in time.