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Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior.
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Aapo Hyvärinen and Peter Dayan · 2005
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Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian · 2011
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Diederik P Kingma and Max Welling · 2013
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Singanallur V Venkatakrishnan, Charles A Bouman, and Brendt Wohlberg · 2013
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Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
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Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
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Imagenet large scale visual recognition challenge
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Plug-and-play admm for image restoration: Fixed-point convergence and applications
Stanley H Chan, Xiran Wang, and Omar A Elgendy · 2016
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Globally and locally consistent image completion
Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa · 2017
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A plug-and-play priors approach for solving nonlinear imaging inverse problems
Ulugbek S Kamilov, Hassan Mansour, and Brendt Wohlberg · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Learning proximal operators: Using denoising networks for regularizing inverse imaging problems
Tim Meinhardt, Michael Moller, Caner Hazirbas, and Daniel Cremers · 2017
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The little engine that could: Regularization by denoising (red)
Yaniv Romano, Michael Elad, and Peyman Milanfar · 2017
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Learning deep cnn denoiser prior for image restoration
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang · 2017
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Efficient bayesian computation by proximal markov chain monte carlo: when langevin meets moreau
Alain Durmus, Eric Moulines, and Marcelo Pereyra · 2018
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
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Plug-and-play image restoration with deep denoiser prior
Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte · 2021
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Joint image compression and denoising via latent-space scalability
Saeed Ranjbar Alvar, Mateen Ulhaq, Hyomin Choi, and Ivan V Bajić · 2022
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Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation
Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, and Pheng-Ann Heng · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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A prior of a googol gaussians: a tensor ring induced prior for generative models
Maxim Kuznetsov, Daniil Polykovskiy, Dmitry P Vetrov, and Alex Zhebrak · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
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Plug-and-play methods provably converge with properly trained denoisers
Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, and Wotao Yin · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Deep plug-and-play super-resolution for arbitrary blur kernels
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2019
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Learning-based noise component map estimation for image denoising
Sheyda Ghanbaralizadeh Bahnemiri, Mykola Ponomarenko, and Karen Egiazarian · 2022
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye · 2022
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Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction
Hyungjin Chung, Byeongsu Sim, and Jong Chul Ye · 2022
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On analyzing generative and denoising capabilities of diffusion-based deep generative models
Kamil Deja, Anna Kuzina, Tomasz Trzciński, and Jakub M Tomczak · 2022
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Learned gradient of a regularizer for plug-and-play gradient descent
Rita Fermanian, Mikael Le Pendu, and Christine Guillemot · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
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Learning discriminative shrinkage deep networks for image deconvolution
Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, and Ming-Hsuan Yang · 2022
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Bayesian imaging using plug & play priors: when langevin meets tweedie
Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, and Marcelo Pereyra · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Deep unfolding with normalizing flow priors for inverse problems
Xinyi Wei, Hans van Gorp, Lizeth Gonzalez-Carabarin, Daniel Freedman, Yonina C Eldar, and Ruud JG van Sloun · 2022
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