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Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels [article]

Kai Zhang, Wangmeng Zuo, Lei Zhang
2019 arXiv   pre-print
To optimize the new degradation induced energy function, we then derive a plug-and-play algorithm via variable splitting technique, which allows us to plug any super-resolver prior rather than the denoiser  ...  In the meanwhile, plug-and-play image restoration has been recognized with high flexibility due to its modular structure for easy plug-in of denoiser priors.  ...  It also turns out that we can plug super-resolver prior rather than denoiser prior into the plug-and-play framework.  ... 
arXiv:1903.12529v1 fatcat:3wgb7l3l7nam5pn3rk4n7zonye

Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels

Kai Zhang, Wangmeng Zuo, Lei Zhang
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
To optimize the new degradation induced energy function, we then derive a plug-and-play algorithm via variable splitting technique, which allows us to plug any super-resolver prior rather than the denoiser  ...  prior as a modular part.  ...  It also turns out that we can plug super-resolver prior rather than denoiser prior into the plug-and-play framework.  ... 
doi:10.1109/cvpr.2019.00177 dblp:conf/cvpr/0008Z019 fatcat:m4j4scmnwncfdgkqyokbjq3rdi

Unified Single-Image and Video Super-Resolution via Denoising Algorithms [article]

Alon Brifman, Yaniv Romano, Michael Elad
2018 arXiv   pre-print
We exploit the Plug-and-Play-Prior framework and the Regularization-by-Denoising (RED) approach that extends it, and show how to use such denoisers in order to handle the SISR and the VSR problems using  ...  In this work we suggest a simple and robust super-resolution framework that can be applied to single images and easily extended to video.  ...  The Plug-and-Play-Prior (PPP) scheme [27] offers a method to separate the two in a manner that allows us to use prior functions that are already integrated into Gaussian denoising algorithms.  ... 
arXiv:1810.01938v1 fatcat:cnbptuimcjbw7poqh5ib5hjvmq

Learning Deep CNN Denoiser Prior for Image Restoration

Kai Zhang, Wangmeng Zuo, Shuhang Gu, Lei Zhang
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems.  ...  Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g  ...  We gratefully acknowledge the support from NVIDIA Corporation for providing us the Titan X GPU used in this research.  ... 
doi:10.1109/cvpr.2017.300 dblp:conf/cvpr/ZhangZGZ17 fatcat:pm25evg7pvhkzita6tjnn2wyku

Learning Deep CNN Denoiser Prior for Image Restoration [article]

Kai Zhang, Wangmeng Zuo, Shuhang Gu, Lei Zhang
2017 arXiv   pre-print
To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems.  ...  Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g  ...  We gratefully acknowledge the support from NVIDIA Corporation for providing us the Titan X GPU used in this research.  ... 
arXiv:1704.03264v1 fatcat:5gec5vbgpbcfvcvp73yg7hmi5m

Deep Model-Based Super-Resolution with Non-uniform Blur [article]

Charles Laroche and Andrés Almansa and Matias Tassano
2022 arXiv   pre-print
To this end, we first propose a fast deep plug-and-play algorithm, based on linearized ADMM splitting techniques, which can solve the super-resolution problem with spatially-varying blur.  ...  Single-image super-resolution methods seek to restore a high-resolution image from blurred, subsampled, and noisy measurements.  ...  Deep plug-and-play methods can be used to solve a large variety of image resoration tasks such as Gaussian denoising [5] , image deblurring [58] or super-resolution [4] .  ... 
arXiv:2204.10109v2 fatcat:wbpsfhqptre3ld6agpdjcunyky

Deep Unfolding Network for Image Super-Resolution [article]

Kai Zhang, Luc Van Gool, Radu Timofte
2020 arXiv   pre-print
As a result, the proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages  ...  Specifically, by unfolding the MAP inference via a half-quadratic splitting algorithm, a fixed number of iterations consisting of alternately solving a data subproblem and a prior subproblem can be obtained  ...  Acknowledgments: This work was partly supported by the ETH Zürich Fund (OK), a Huawei Technologies Oy (Finland) project, an Amazon AWS grant, and Nvidia.  ... 
arXiv:2003.10428v1 fatcat:jbnctobc2reujhqcy6dyaypssm

Multi-Resolution Data Fusion for Super Resolution Imaging [article]

Emma J Reid, Lawrence F Drummy, Charles A Bouman, Gregery T Buzzard
2022 arXiv   pre-print
We use MACE to prove that using a mismatched back-projector is equivalent to using a standard back-projector and an appropriately modified prior model.  ...  Our approach uses small quantities of unpaired high-resolution data to train a neural network prior model denoiser and then uses the Multi-Agent Consensus Equilibrium (MACE) problem formulation to balance  ...  Hampton for assistance in data acquisition and Asif Mehmood for his mentorship.  ... 
arXiv:2105.06533v6 fatcat:m6dte5ul3zanve2xcdc7zvn4za

The Little Engine that Could: Regularization by Denoising (RED) [article]

Yaniv Romano, Michael Elad, Peyman Milanfar
2017 arXiv   pre-print
Recent work has answered this question positively, in the form of the Plug-and-Play Prior (P^3) method, showing that any inverse problem can be handled by sequentially applying image denoising steps.  ...  We test this approach and demonstrate state-of-the-art results in the image deblurring and super-resolution problems.  ...  We leave this and other possibilities of formulating the regularization with the use of f (x) for future work. When is Plug-and-Play-Prior = RED ?  ... 
arXiv:1611.02862v3 fatcat:i6haon6za5dxbmpgkgoyevc57a

Deep Learning on Image Denoising: An overview [article]

Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin
2020 arXiv   pre-print
In this paper, we offer a comparative study of deep techniques in image denoising.  ...  Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis.  ...  [262] proposed to use cascaded deblurring and singleimage super-resolution (SISR) networks to recover plug-and-play super-resolution images.  ... 
arXiv:1912.13171v4 fatcat:4ts2xpivhreptelbgeqhljjiri

The Little Engine That Could: Regularization by Denoising (RED)

Yaniv Romano, Michael Elad, Peyman Milanfar
2017 SIAM Journal of Imaging Sciences  
The recent work by Venkatakrishnan, Bouman, and Wohlberg provides a positive and tantalizing answer to this question, in the form of the Plug-and-Play Prior (P 3 ) method [30, 31, 32, 33] .  ...  Recent work has answered this question positively, in the form of the Plug-and-Play Prior (P 3 ) method, showing that any inverse problem can be handled by sequentially applying image denoising steps.  ...  We leave this and other possibilities of formulating the regularization with the use of f (x) for future work. When is Plug-and-Play-Prior = RED?  ... 
doi:10.1137/16m1102884 fatcat:axnnnf6szzhidjzeyuhpxze3gi

Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion [article]

Huanfeng Shen, Menghui Jiang, Jie Li, Chenxia Zhou, Qiangqiang Yuan, Liangpei Zhang
2021 arXiv   pre-print
The data-driven methods have a stronger prior knowledge learning capability for huge data, especially for nonlinear statistical features; however, the interpretability of the networks is poor, and they  ...  This paper also gives some new insights into the potential future directions, in terms of both methods and applications.  ...  Most of the existing plug-and-play prior based image restoration methods treat the CNN Gaussian denoiser as the prior [11, 22] , and some treat the CNN super-resolver as the prior [12] . C.  ... 
arXiv:2108.06073v1 fatcat:vdnabzwvvnbvnllrlvzvvqgolm

Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts

Bruno Lecouat, Jean Ponce, Julien Mairal
2021 2021 IEEE/CVF International Conference on Computer Vision (ICCV)  
Output of Dcraw Camera's ISP (hi-quality jpeg) Joint demosaick+single-image SR Proposed method Figure 1 : ×4 super-resolution results obtained from a burst of 30 raw images acquired with a handheld Panasonic  ...  Lumix GX9 camera at 12800 ISO for the top image and 25600 for the bottom image.  ...  Acknowledgments We thank Frédéric Guichard for useful discussions and comments.  ... 
doi:10.1109/iccv48922.2021.00237 fatcat:dhtqfk5cnveyhfv3z4ahetdaw4

Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts [article]

Bruno Lecouat, Jean Ponce, Julien Mairal
2021 arXiv   pre-print
/learning an image prior (regularizer) well suited to the task.  ...  The effectiveness of our approach is demonstrated on synthetic and real image bursts, setting a new state of the art on several benchmarks and delivering excellent qualitative results on real raw bursts  ...  We provide a quantitative comparison in Table 1 with the model introduced in [4] , as well as a single-image upsampling baseline based on the ResUNet architecture [48] , which we use as a plug-and-play  ... 
arXiv:2104.06191v2 fatcat:zehpbseaqvanbcelur4pik32wu

Variational Image Restoration Network [article]

Zongsheng Yue, Hongwei Yong, Qian Zhao, Lei Zhang, Deyu Meng
2021 arXiv   pre-print
Extensive experiments demonstrate the superiority of the proposed method on three classical image restoration tasks, including image denoising, image super-resolution and JPEG image deblocking.  ...  Secondly, existing DL methods are mostly trained on one pre-assumed degradation process for all of the training image pairs, such as the widely used bicubic downsampling assumption in the image super-resolution  ...  IRCNN is a non-blind plug-and-play method which embeds a deep denoiser into the traditional HQS algorithm.  ... 
arXiv:2008.10796v2 fatcat:dhvssrvlyrg6pobgflvrtbbb4a
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