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Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising
2021
Electronics
Preserved Fulltext
Motivated by the performance of deep learning in several medical imaging tasks, a deep learning-based denoising method Cross-Modality Guided Denoising Network CMGDNet for removing Rician noise in T1-weighted ...
The ensemble learning methods using cross-modal medical imaging adds reliability to several medical image analysis tasks. ...
Few research works presented for medical image denoising [23, 40] show improved performance over their single image denoising counterparts. ...
doi:10.3390/electronics10222855
fatcat:qxivy4pezvdablfxdsefabqduu
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
[article]
2019
arXiv
pre-print
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Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing. ...
Additionally, we can show that the input image's content is almost unchanged after the processing which is not the case for conventional deep learning approaches. ...
We demonstrate this based on two popular tasks in natural and medical imaging processing: image super resolution (SR) [20] , [21] , [22] and denoising [23] , [24] , [25] . ...
arXiv:1911.07731v1
fatcat:xwvcuyv5fjde3nkmprfacedfje
Probabilistic self-learning framework for Low-dose CT Denoising
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.00327v1 fatcat:xxhovo4whrgxrfzwjygbgemx7q
Supervised deep learning can be used to train a neural network to denoise the low-dose CT (LDCT). ...
To alleviate this problem, in this paper, a shift-invariant property based neural network was devised to learn the inherent pixel correlations and also the noise distribution by only using the LDCT images ...
Code Availability The code would be publicly released via https://github.com/baiti01/LDCT-probabilistic-self-learning once this work is accepted for publication. ...
arXiv:2006.00327v2
fatcat:tw2whydzfrhwrms6iacqntfa2y
OpenDenoising: an Extensible Benchmark for Building Comparative Studies of Image Denoisers
[article]
2019
arXiv
pre-print
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Image denoising has recently taken a leap forward due to machine learning. ...
However, image denoisers, both expert-based and learning-based, are mostly tested on well-behaved generated noises (usually Gaussian) rather than on real-life noises, making performance comparisons difficult ...
These methods are evaluated on well-behaved noises (typically AWGN) for the tests to be easily reproducible. Only Noise2Void is evaluated on medical images subject to complex noise. ...
arXiv:1910.08328v1
fatcat:3z6shbbb2fca5ag7brbdewnhr4
Opendenoising: An Extensible Benchmark for Building Comparative Studies of Image Denoisers
2020
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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These methods are evaluated on well-behaved noises (typically AWGN) for the tests to be easily reproducible. Only Noise2Void is evaluated on medical images subject to complex noise. ...
Interception strongly damages the images and denoising is necessary to interpret their content. For reproducibility, we released the dataset used for this study 4 . ...
doi:10.1109/icassp40776.2020.9053937
dblp:conf/icassp/LemarchandMPN20
fatcat:fuhz6hnchndgjnxe3ycouwbyua
An Improved Combination of Image Denoisers Using Spatial Local Fusion Strategy
2020
IEEE Access
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We combine the aforementioned denoisers to generate image sources for fusion owing to their strong complementary nature. ...
learn an explicit mapping of Eq. (7), which puts the noise level and noise image in the input. ...
doi:10.1109/access.2020.3016766
fatcat:g3v4uvm2tvfslmwrqyp5ahxaau
Boosting of Denoising Effect with Fusion Strategy
2020
Applied Sciences
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Image denoising, a fundamental step in image processing, has been widely studied for several decades. ...
The boosting process is formulated as an adaptive weight-based image fusion problem by preserving the details for the initial denoised images output by the NCSR and the DnCNN. ...
Acknowledgments: The authors would like to thank the anonymous reviewers and AE for their constructive comments and suggestions, which improve the quality of the paper. ...
doi:10.3390/app10113857
fatcat:kjnhiwenq5fl7ffyu3smna552u
Unsupervised Image Noise Modeling with Self-Consistent GAN
[article]
2020
arXiv
pre-print
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However, existing deep learning methods for noise modeling generally require clean and noisy image pairs for model training; these image pairs are difficult to obtain in many realistic scenarios. ...
They jointly facilitate unsupervised learning of a noise model for various noise types. ...
Existing deep learning methods for medical image denoising generally require a considerable amount of clean and noisy image pairs for model training. ...
arXiv:1906.05762v3
fatcat:dh6doqe5k5b6viuysk7iw2qui4
Image Denoising Using Nonlocal Regularized Deep Image Prior
2021
Symmetry
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However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging ...
Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. ...
This work was also supported by the Program for Innovative Research Team of Huizhou University (IRTHZU). ...
doi:10.3390/sym13112114
fatcat:bdvu6fpeszb2vhquc7clpggx3i
Unsupervised Image Denoising with Frequency Domain Knowledge
[article]
2021
arXiv
pre-print
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Supervised learning-based methods yield robust denoising results, yet they are inherently limited by the need for large-scale clean/noisy paired datasets. ...
The use of unsupervised denoisers, on the other hand, necessitates a more detailed understanding of the underlying image statistics. ...
However, our method removes the noise, while preserving the details of organs. It shows that our method is also practical for medical image denoising. ...
arXiv:2111.14362v1
fatcat:aoq7lx3uhrgpnkuu5okpdr7jei
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
[chapter]
2018
Lecture Notes in Computer Science
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To make up for the absence of high quality CT images, we detail how to synthesize a large number of low-and high-quality natural images and use transfer learning with progressively larger amounts of CT ...
While many advancements have been made, there is room for continued improvements. One hurdle is that CT images can exhibit high noise and low contrast, particularly in lower dosages. ...
We thank Nvidia for GPU card donation. ...
doi:10.1007/978-3-030-00919-9_6
fatcat:2mr7dtom6jd3ldd6hmnnbh6z7a
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
[article]
2018
arXiv
pre-print
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To make up for the absence of high quality CT images, we detail how to synthesize a large number of low- and high-quality natural images and use transfer learning with progressively larger amounts of CT ...
While many advancements have been made, there is room for continued improvements. One hurdle is that CT images can exhibit high noise and low contrast, particularly in lower dosages. ...
We thank Nvidia for GPU card donation. ...
arXiv:1807.07144v1
fatcat:5ipjnl3nibemjdsp2usxpdhd2q
A Modified Iterative Alternating Direction Minimization Algorithm for Impulse Noise Removal in Images
2014
Journal of Applied Mathematics
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Simulation results demonstrate that the proposed algorithm outperforms typical denoising methods in terms of preserving edges and textures for both salt-and-pepper noise and random-valued impulse noise ...
Images are often corrupted by impulse noise. ...
Denoising performance of the proposed method may be further improved if one can learn a proper similarity from serious noisy image. Figure 1 :Figure 2 : 12 Images with impulse noise. ...
doi:10.1155/2014/595782
fatcat:nlpho2xnb5hhbb2uxsklu6unuu
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
[article]
2021
arXiv
pre-print
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In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. ...
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference ...
Self-supervised deep learning for denoising 1) Background on denoising using deep learning: Image denoising concerns a special case of the acquisition model in Eq. ...
arXiv:2105.08040v2
fatcat:56gnjk7y45a7jifx4s6npb6zxy
A survey on deep learning-based Monte Carlo denoising
2021
Computational Visual Media
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Recent years have seen increasing attention and significant progress in denoising MC rendering with deep learning, by training neural networks to reconstruct denoised rendering results from sparse MC samples ...
AbstractMonte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its flexibility and generality. ...
[65] modified surface-based MC denoisers for path-traced visualizations of medical volumetric data. ...
doi:10.1007/s41095-021-0209-9
fatcat:pki3mbbpw5fjxf6oaphcnlto4a
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