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2020 IEEE Transactions on Image Processing  
Xing, and Z. Li 3927 A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal From Images on Graphs ...................................................................  ...  Li 5431 A New Polyphase Down-Sampling-Based Multiple Description Image Coding ............................................. ...................................................................... S.  ...  Lin, and Zhang, Y. Tian, K. Wang, W. Zhang, and F.-  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa

Table of Contents

2020 IEEE Signal Processing Letters  
Murala 675 Rank-One Matrix Approximation With p -Norm for Image Inpainting . . . . . . . . . . . . . . . . . . . . X. P. Li, Q. Liu, and H. C.  ...  So 680 Blind Quality Index of Depth Images Based on Structural Statistics for View Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Gao, and H. Li 1470 Min-Max Average Pooling Based Filter for Impulse Noise Removal . . . . . . . . . . . . . . . . . .P. Satti, N. Sharma, and B.  ... 
doi:10.1109/lsp.2020.3040840 fatcat:ezrfzwo6tjbkfhohq2tgec4m6y

A Dictionary Learning Approach for Poisson Image Deblurring

Liyan Ma, L. Moisan, Jian Yu, Tieyong Zeng
2013 IEEE Transactions on Medical Imaging  
The restoration of images corrupted by blur and Poisson noise is a key issue in medical and biological image processing.  ...  While most existing methods are based on variational models, generally derived from a Maximum A Posteriori (MAP) formulation, recently sparse representations of images have shown to be efficient approaches  ...  ACKNOWLEDGEMENTS The authors also thank the anonymous reviewers for their extremely useful suggestions for improving the quality of the paper.  ... 
doi:10.1109/tmi.2013.2255883 pmid:23549888 fatcat:ayjr7tgjhjcbrdsiqirjq5zy3i

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 3790-3804 A New Polyphase Down-Sampling-Based Multiple Description Image Coding.  ...  ., +, TIP 2020 3374-3387 A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal From Images on Graphs.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
., +, LSP 2020 121-125 A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network. Song, G., +, LSP 2020 451-455 A Sparse Conjugate Gradient Adaptive Filter.  ...  ., +, LSP 2020 196-200 Distortion Blind Quality Index of Depth Images Based on Structural Statistics for View Synthesis.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

Image Restoration for Remote Sensing: Overview and Toolbox [article]

Benhood Rasti, Yi Chang, Emanuele Dalsasso, Loïc Denis, Pedram Ghamisi
2021 arXiv   pre-print
The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts.  ...  This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration  ...  We should add that problem (2) can be defined subject to equality and inequality constraints. Multiplicative noise is typical in coherent imaging systems such as SAR or ultrasound imaging.  ... 
arXiv:2107.00557v2 fatcat:adn5fpdza5h4tbsycg7yw6rqzu

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges.  ...  In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation pro-  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation [article]

Petteri Teikari, Marc Santos, Charissa Poon, Kullervo Hynynen
2016 arXiv   pre-print
Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images.  ...  We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks.  ...  Acknowledgements We would like to thank Sharan Sankar for his work as a summer student writing wrapper for various wrappers for ITK C++ functions.  ... 
arXiv:1606.02382v1 fatcat:v5jwomv4gbf7bhxe6yn2tbkhiq

Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review

Paul Rodríguez
2013 Journal of Electrical and Computer Engineering  
Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution  ...  , and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models.  ...  Moreover, a framework based on MRF with levelable priors for restoration of images corrupted by Gaussian or Speckle (Rayleigh) was proposed in [112] where only the denoising problem was addressed, and  ... 
doi:10.1155/2013/217021 fatcat:kfqamwddznbk5jnjneieuaxdlu

Bilevel approaches for learning of variational imaging models [article]

Luca Calatroni, Cao Chung, Juan Carlos De Los Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen
2015 arXiv   pre-print
We review some recent learning approaches in variational imaging, based on bilevel optimisation, and emphasize the importance of their treatment in function space.  ...  Based on this information, Newton type methods are studied for the solution of the problems at hand, combining them with sampling techniques in case of large databases.  ...  In [43] a combined L 1 -L 2 TV-based model is considered for impulse and Gaussian noise removal.  ... 
arXiv:1505.02120v1 fatcat:zbj34jpfsbetxkb2fjc2ef2674

Computer-Aided Diagnostics and Pattern Recognition: Automated Glaucoma Detection [chapter]

Thomas Köhler, Rüdiger Bock, Joachim Hornegger, Georg Michelson
2014 Teleophthalmology in Preventive Medicine  
This chapter reviews and discusses recent advances in the development of pattern recognition algorithms for automated glaucoma detection based on structural retinal image data.  ...  Glaucoma is one of the major causes for blindness with a high rate of unreported cases. To reduce this number, screening programs are performed.  ...  Acknowledgment The authors gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German National Science Foundation (DFG) in the framework of the  ... 
doi:10.1007/978-3-662-44975-2_9 fatcat:dawhtu7gsvasrmyeg4j3h3crna

Review: Deep Learning in Electron Microscopy [article]

Jeffrey M. Ede
2020 arXiv   pre-print
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity.  ...  We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a creative commons 4.0 73 license.  ... 
arXiv:2009.08328v4 fatcat:umocfp5dgvfqzck4ontlflh5ca

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks [article]

Michael T. McCann, Michael Unser
2019 arXiv   pre-print
The first are the classical direct methods, including Tikhonov regularization; the second are the variational methods based on sparsity and the theory of compressive sensing; and the third are the learning-based  ...  In the last few years, machine learning-based approaches have shown impressive performance on image reconstruction problems, triggering a wave of enthusiasm and creativity around the paradigm of learning  ...  Chapter 4 Sparsity-Based Image Reconstruction Over the past two decades, numerous new reconstruction methods have emerged, many of which continue to use the variational framework, i.e., reconstruction  ... 
arXiv:1901.03565v2 fatcat:bt7bf5iq2vhntny7r4lshiom7e

Structured Compressed Sensing: From Theory to Applications

Marco F. Duarte, Yonina C. Eldar
2011 IEEE Transactions on Signal Processing  
In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS.  ...  Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity.  ...  Xie, and the anonymous reviewers for offering many helpful comments on an early draft of the manuscript, and to P. L. Dragotti  ... 
doi:10.1109/tsp.2011.2161982 fatcat:mlbtksjmqvbmfmi6gpxwtunbti

Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning [article]

Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
2019 arXiv   pre-print
These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models.  ...  A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics.  ...  Beyond the medical imaging applications, such approaches have been successfully used for super-resolution microscopy [75] , image inpainting problems [76] , image impulse noise removal [77] , etc.  ... 
arXiv:1904.02816v2 fatcat:ehahzrib2ff3dl5yl6pa7xpf24
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