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A CNN-based Prediction-Aware Quality Enhancement Framework for VVC

Fatemeh Nasiri, Wassim Hamidouche, Luce Morin, Nicolas Dhollande, Gildas Cocherel
2021 IEEE Open Journal of Signal Processing  
The motivation is that normative decisions made by the encoder can significantly impact the type and strength of artifacts in the decoded images.  ...  Furthermore, to retain a low memory requirement for the proposed method, one model is used for all Quantization Parameters (QPs) with a QP-map, which is also shared between luma and chroma components.  ...  SEFCNN IEEE-TCSVT 19 [43] DIV2K QP CT ILF Optional ILF with adaptive net. selection for different CT and distortion levels.  ... 
doi:10.1109/ojsp.2021.3092598 fatcat:dqgzponwuza7taqsjalo36rgby

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms [article]

Yanna Bai, Wei Chen, Jie Chen, Weisi Guo
2020 arXiv   pre-print
We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods  ...  Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance  ...  (b) The final model of DRCN with recursive-supervision and skip connection. The reconstruction network is shared for recursive predictions.  ... 
arXiv:2007.13290v2 fatcat:kqoerts77nftbl32fctx3za2me

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2020 Frontiers in Cardiovascular Medicine  
directions for future research.  ...  In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research.  ...  ACKNOWLEDGMENTS We would like to thank our colleagues: Karl Hahn, Qingjie Meng, James Batten, and Jonathan Passerat-Palmbach who provided the insight and expertise that greatly assisted the work, and also  ... 
doi:10.3389/fcvm.2020.00025 pmid:32195270 pmcid:PMC7066212 fatcat:iw7xpnltn5cgbn5ullq2ldy3nq

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Yasarla, R., +, TIP 2021 6570-6582 TPSSI-Net: Fast and Enhanced Two-Path Iterative Network for 3D SAR Sparse Imaging.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image [article]

Xiaoxiao He, Chaowei Tan, Virak Tan, Kang Li
2022 arXiv   pre-print
The proposed neural network and the recursive learning scheme improve the overall quality of the segmentation on humerus and scapula on the low-resolution dataset and reduced incorrect segmentation in  ...  framework that iterative utilize the generated labels for reducing the errors among segmentations and increase our dataset set for training the next round network.  ...  Left is an abstract architecture for LSTM as a recurrent neural network. Right is the proposed recursive learning framework. Fig. 6 . 6 Fig. 6.  ... 
arXiv:2203.07846v1 fatcat:gztzga6umvh3np4sya3upc7otq

Deep Learning in Cardiology

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.  ...  Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data.  ...  [76] used WT to remove high frequency noise and baseline drift and biorthogonal spline wavelet for detecting the R-peak.  ... 
doi:10.1109/rbme.2018.2885714 fatcat:pa47trmskvflvig5cotth265q4

Current development and prospects of deep learning in spine image analysis: a literature review

Biao Qu, Jianpeng Cao, Chen Qian, Jinyu Wu, Jianzhong Lin, Liansheng Wang, Lin Ou-Yang, Yongfa Chen, Liyue Yan, Qing Hong, Gaofeng Zheng, Xiaobo Qu
2021 Quantitative Imaging in Medicine and Surgery  
However, it is still difficult for clinicians and technicians to fully understand this rapidly evolving field due to the diversity of applications, network structures, and evaluation criteria.  ...  However, further exploration is needed in terms of data sharing, functional information, and network interpretability. The DL technique is a powerful method for spine image analysis.  ...  The recurrent neural network (RNN) is primarily designed for sequence data processing (51) .  ... 
doi:10.21037/qims-21-939 pmid:35655825 pmcid:PMC9131328 fatcat:k2njqeqo4vdvhatcq24hvgmzta

Learning Enriched Features for Fast Image Restoration and Enhancement [article]

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao
2022 arXiv   pre-print
across the multi-resolution streams, (c) non-local attention mechanism for capturing contextual information, and (d) attention based multi-scale feature aggregation.  ...  This paper presents a new architecture with a holistic goal of maintaining spatially-precise high-resolution representations through the entire network, and receiving complementary contextual information  ...  Scale-recurrent network for deep image deblurring. In CVPR, 2018. 1 [12] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multiscale convolutional neural network for dynamic scene deblurring.  ... 
arXiv:2205.01649v1 fatcat:hagwkoo5b5dahkv5apc5lmwtom

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [article]

Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen (+58 others)
2021 arXiv   pre-print
(MICCAI) in 2019 and 2020, with a call for algorithms towards labelling and segmentation of vertebrae.  ...  In this work, we present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.  ...  Preprocessing CT images reconstructed from low-dose acquisitions may be severely degraded with noise and streak artifacts due to quantum noise, or with view-aliasing artifacts due to insufficient angular  ... 
arXiv:2001.09193v4 fatcat:sjffhailrncmhnigluleejpwwi

Video Super Resolution Based on Deep Learning: A Comprehensive Survey [article]

Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, Radu Timofte
2022 arXiv   pre-print
It is well known that the leverage of information within video frames is important for video super-resolution.  ...  To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our  ...  Acknowledgment We thank all the reviewers for their valuable comments. We would like to thank Mr. Zekun Li (Master student at School of Artificial Intelligence in Xidian University) and Dr.  ... 
arXiv:2007.12928v3 fatcat:nxoejcfdnzas3jznbqsale36ty

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
promising directions for the Medical Imaging Community to fully harness deep learning in the future.  ...  INDEX TERMS Deep learning, medical imaging, artificial neural networks, survey, tutorial, data sets. 99540 2169-3536  ...  [168] proposed an FCN based technique for the automatic vetebra segmentation in CT images. The underlying architecture of their network is inspired by U-Net.  ... 
doi:10.1109/access.2019.2929365 fatcat:arimcbjaxrd3zcsjyzd7abjgd4

CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020

2020 International Journal of Computer Assisted Radiology and Surgery  
The traditional platforms of CARS Congresses for the scholarly publication and communication process for the presentation of R&D ideas were congress centers or hotels, typically hosting 600-800 participants  ...  the exchange/communication of R&D ideas by means of verbal and written statements made by responsible authors, scrutinized by informed reviewers and utilized by an open-minded audience, with the aim to  ...  Acknowledgements This study was funded by the National Key R&D Program, China (2017YFC0110502) Acknowledgments This was study was supported partly by the NIH/NIBIB Grant of R21EB024025, R01EB023942, and  ... 
doi:10.1007/s11548-020-02171-6 pmid:32514840 fatcat:lyhdb2zfpjcqbf4mmbunddwroq

2020 Index IEEE Transactions on Biomedical Engineering Vol. 67

2020 IEEE Transactions on Biomedical Engineering  
for EEG Sensor Networks With Application to Auditory Attention Detection; TBME Jan. 2020 234-244 Natarajan, K., see Chandrasekhar, A., TBME Nov. 2020 3134-3140 Nath, M., see Maity, S., TBME Dec. 2020  ...  ., and Heldt, T., A Time-Frequency Approach for Cerebral Embolic Load Monitoring; TBME April 2020 1007-1018 Imaduddin, S.M., Fanelli, A., Vonberg, F.W., Tasker, R.C., and Heldt, T., Pseudo-Bayesian Model-Based  ...  Phellan, R., +, TBME July 2020 1936-1946 Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.  ... 
doi:10.1109/tbme.2020.3048339 fatcat:y7zxxew27fgerapsnrhh54tm7y

Review: Deep Learning in Electron Microscopy [article]

Jeffrey M. Ede
2020 arXiv   pre-print
We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.  ...  For context, we review popular applications of deep learning in electron microscopy.  ...  Acknowledgements Thanks go to Jeremy Sloan and Martin Lotz for internally reviewing this article.  ... 
arXiv:2009.08328v4 fatcat:umocfp5dgvfqzck4ontlflh5ca

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, and article number.  ...  ., +, TIM 2021 2500314 Waterdrop Removal From Hot-Rolled Steel Strip Surfaces Based on Progressive Recurrent Generative Adversarial Networks.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry
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