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

Yaniv Romano, Michael Elad, Peyman Milanfar
2017 arXiv   pre-print
As opposed to the P^3 method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regularization of the inverse problem.  ...  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.  ...  This led us to the proposed scheme, termed Regularization by Denoising (RED).  ... 
arXiv:1611.02862v3 fatcat:i6haon6za5dxbmpgkgoyevc57a

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

Yaniv Romano, Michael Elad, Peyman Milanfar
2017 SIAM Journal of Imaging Sciences  
As opposed to the P 3 method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regularization of the inverse problem.  ...  First, this method is not always accompanied by a clear definition of the objective function, since the regularization being effectively used is only implicit, implied by the denoising algorithm.  ...  This is the Regularization by Denoising (RED) paradigm that this work advocates.  ... 
doi:10.1137/16m1102884 fatcat:axnnnf6szzhidjzeyuhpxze3gi

Sparse Multichannel Blind Deconvolution with Regularization by Denoising

2021 Proceeding of the 17th International Congress of the Brazilian Geophysical   unpublished
Romano, Y., Elad, M., and Milanfar, P., 2016, The little engine that could: Regularization by denoising (RED): Iqbal, N., Liu, E  ...  This is a problem that, in signal the Regularization by Denoising Engine proposed by processing, can be modeled as a SIMO (single input, Romano et al. (2016).  ... 
doi:10.22564/17cisbgf2021.333 fatcat:thdgwjozazdgfh5egzegvog76e

Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data [article]

Nasser Kazemi
2020 arXiv   pre-print
Accordingly, by incorporating feed-forward de-noising convolutional neural networks (DnCNN) in regularization by de-noising regularizer, we formulate two transferable optimization problems for de-noising  ...  To remedy this shortcoming, we have developed a workflow that transfers the learned operator from the camera images to the seismic domain, without modifying its training parameters.  ...  The regularizer R(s), in Equation (7) , is called regularization by de-noising (RED) [35] . Note that the RED function promotes orthogonality between the predicted noise and the input.  ... 
arXiv:2007.10250v1 fatcat:wbgiiq7cfbdudavfr4cye7juke

Regularization by Denoising applied to non-linear traveltime tomography

Andres A Ambros Vargas
2020
The recently published Regularization by Denoising from the signal processing field proposes to take advantage of the existing powerful denoising algorithms developed for the removal of Gaussian noise  ...  The solution of an inverse problem such as the traveltime tomography requires a regularization function that constrains the solution and stabilizes the inversion.  ...  Acknowledgements I want to thank my supervisor, Dr Mauricio Sacchi, for his time and attention and guidance on the development of this thesis.  ... 
doi:10.7939/r3-zx7v-s156 fatcat:tgjo3frcurh63avkyge4ld453q

Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination

Dari Kimanius, Gustav Zickert, Takanori Nakane, Jonas Adler, Sebastian Lunz, Carola-Bibiane Schönlieb, Ozan Öktem, Sjors H. W. Scheres
2021 IUCrJ  
This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising.  ...  The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space.  ...  (UKRI) Strategic Priorities Fund under the EPSRC (EP/T001569/1), particularly the 'AI for Science' theme within that grant and The Alan Turing Institute.  ... 
doi:10.1107/s2052252520014384 pmid:33520243 pmcid:PMC7793004 fatcat:geajtr5lsfdszl2xsetuktsyb4

Spatially Adaptive Regularizer for Mesh Denoising

Xuan Cheng, Yinglin Zheng, Yuhui Zheng, Fang Chen, Kunhui Lin
2020 IEEE Access  
Many existing methods formulate the mesh denoising as an optimization problem, whereby the optimized mesh could best fit both the input and a set of constraints defined as an L p norm regularizer.  ...  We compare the proposed method with state-of-the-arts in both synthetic and real-scanned benchmark datasets, and show that the proposed method could produce comparable results to neural network based mesh  ...  ACKNOWLEDGMENT The authors would like to thank Z. Liu for providing us the denoised results of TVNF [26] and ASONF [29] on the benchmark data.  ... 
doi:10.1109/access.2020.2987046 fatcat:erv6r2ly6jbg7pd53hijhtenoa

Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm [article]

Yu Sun, Shiqi Xu, Yunzhe Li, Lei Tian, Brendt Wohlberg, Ulugbek S. Kamilov
2018 arXiv   pre-print
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm  ...  We validate the algorithm by showing that it can lead to significant performance gains on both simulated and experimental data.  ...  Milanfar, “The little engine that could: Regularization by denoising (RED),” SIAM J. Imaging Sci., vol. 10, no. 4, pp. 1804–1844, 2017. [24] C. Metzler, P. Schniter, A. Veeraraghavan, and R.  ... 
arXiv:1811.00120v2 fatcat:c3hl2itrcbftpop5pt564n4lky

Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination [article]

Dari Kimanius, Gustav Zickert, Takanori Nakane, Jonas Adler, Sebastian Lunz, Carola-Bibiane Schonlieb, Ozan Oktem, Sjors Scheres
2020 bioRxiv   pre-print
We insert this neural network into the iterative cryo-EM structure determination process through an approach that is inspired by Regularisation by Denoising.  ...  The most popular cryo-EM software solutions to date rely on a regularisation approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space.  ...  C.B.S. acknowledges support from the Leverhulme Trust project on 'Breaking the non-convexity barrier', the Engineering and Physical Sciences Research Council (  ... 
doi:10.1101/2020.03.25.007914 fatcat:5pglr3zwpbchbbeiazhu4veqcu

Analysis of filtering methods for 3D acceleration signals in body sensor network

Wei-zhong Wang, Yan-wei Guo, Bang-yu Huang, Guo-ru Zhao, Bo-qiang Liu, Lei Wang
2011 International Symposium on Bioelectronics and Bioinformations 2011  
We found that (1) Kalman filter showed the largest SNR and R values, followed by median filter, discrete wavelet package shrinkage and finally Butterworth low-pass filter; (2) Realtime performance of median  ...  In this study, firstly 3D acceleration signals were captured by self-developed nine-axis wireless BSN platform during 12 subjects performing regular walking gait.  ...  The probable reason for this abnormal phenomenon might be the waveform delay caused by the filter, which could be easily observed in Fig.5-7 .  ... 
doi:10.1109/isbb.2011.6107697 fatcat:uq7uda6g5rhkfowuovv4uyp3qu

Image denoising: Can plain neural networks compete with BM3D?

H. C. Burger, C. J. Schuler, S. Harmeling
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods.  ...  The best currently available denoising methods approximate this mapping with cleverly engineered algorithms.  ...  We found that we could improve results slightly by weighting the denoised patches with a Gaussian window.  ... 
doi:10.1109/cvpr.2012.6247952 dblp:conf/cvpr/BurgerSH12 fatcat:upgh2k7vxragjkki7rpqzx2vm4

Speckle Noise Removal Convex Method using Higher-order Curvature Variation

Baoxiang Huang, Yunping Mu, Zhenkuan Pan, Li Bai, Huan Yang, Jinming Duan
2019 IEEE Access  
The results indicate that our proposed high order total curvature regularization has certain advantage compared with TV regularization and Lee filter denoising method. B.  ...  The denoising horizontal (blue) and vertical slice curve (red) can overlap with original slice curve (green) almost, consequently, the proposed model can preserve the edge and corner of image.  ...  BAOXIANG HUANG received the M.S. degree in mechatronic engineering from Shandong University, China, in 2005, and the Ph.D. degree in computer engineering from the Ocean University of China, China.  ... 
doi:10.1109/access.2019.2923067 fatcat:pgpwcq6omzdb5lt4cjblljeika

Sparse Sampling for Real-time Ray Tracing

Timo Viitanen, Matias Koskela, Kalle Immonen, Markku Mäkitalo, Pekka Jääskeläinen, Jarmo Takala
2018 Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
We review recent work on image reconstruction from arbitrarily distributed samples, and argue that these will play major role in the future of real-time ray tracing, allowing a larger fraction of samples  ...  ., foveated rendering where sampling is directed by eye tracking. Uneven sampling methods tend to require at least one sample per pixel, limiting their use in real-time rendering.  ...  ACKNOWLEDGEMENTS We would like to thank Frank Meinl for the  ... 
doi:10.5220/0006655802950302 dblp:conf/grapp/ViitanenKIMJT18 fatcat:4likzzmtijgw3imkvjs5jsogqm

Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm [article]

Ti Bai, Biling Wang, Dan Nguyen, Steve Jiang
2020 arXiv   pre-print
Experimental results show that the proposed denoiser can run in as little as 39 ms, which is around 11.6 times faster than the baseline model.  ...  To tackle this problem, we have developed a real time, deep learning based dose denoiser that can be plugged into a current GPU based MC dose engine to enable real time MC dose calculation.  ...  As such, we expect that an MC dose engine featuring our deep dose plugin could be routinely used in a modern TPS for fast yet accurate dose calculations.  ... 
arXiv:2011.14959v2 fatcat:cpq2pbcnyzgw7lly6tyzyyg4vy

Sparsity-based edge noise removal from bilevel graphical document images

Thai V. Hoang, Elisa H. Barney Smith, Salvatore Tabbone
2013 International Journal on Document Analysis and Recognition  
This paper presents a new method to remove edge noise from graphical document images using geometrical regularities of the graphics contours that exist in the images.  ...  Denoising is understood as a recovery problem and is accomplished by employing a sparse representation framework in the form of a basis pursuit denoising algorithm.  ...  The black dotted curve is the function O(·, 0) (no regularization), whereas the red plain curve corresponds to O(·, λ).  ... 
doi:10.1007/s10032-013-0213-4 fatcat:67bx6fazjbad5jkgmygtmptd6e
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