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Analysis of deep learning architectures for turbulence mitigation in long-range imagery
2020
Artificial Intelligence and Machine Learning in Defense Applications II
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For the task of turbulence mitigation, real life data is difficult to obtain, as a clean undistorted image is not always obtainable. ...
The use of deep learning for image processing has now become commonplace, with neural networks being able to outperform traditional methods in many fields. ...
The networks under review were originally designed for tasks such as image denoising, super resolution or image deblurring. ...
doi:10.1117/12.2573927
fatcat:anz7ilqiybdyvflomztzoovuam
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
[article]
2021
arXiv
pre-print
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Recently, neural architecture search (NAS) has been applied to automatically search high-performance networks for medical image segmentation. ...
Existing methods either require long searching time for large-scale 3D image datasets, or are limited to pre-defined topologies (such as U-shaped or single-path). ...
VNet [26] improves 3D UNet with residual blocks. UNet++ [47] uses dense blocks [13] to redesign skip connections. ...
arXiv:2103.15954v1
fatcat:a77g2iy6s5gftmgmz5dqzfn26e
Limited View Tomographic Reconstruction using a Cascaded Residual Dense Spatial-Channel Attention Network with Projection Data Fidelity Layer
2021
IEEE Transactions on Medical Imaging
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In this work, we propose a cascaded residual dense spatial-channel attention network consisting of residual dense spatial-channel attention networks and projection data fidelity layers. ...
In addition, our experimental results on Deep Lesion datasets demonstrate that our method is able to generate high-quality reconstruction for 8 major lesion types. ...
[36] recently also demonstrated that spatial-channel attention can boost the image super-resolution performance. ...
doi:10.1109/tmi.2021.3066318
pmid:33729929
fatcat:dudx7o6zyjgkrekabq2kfjoiqy
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2007.14556v2 fatcat:6t33ikfxrrdkhpxtzgejyzcm4iOther Versions
Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results ...
Our DSNet contains a special Detail Representation Transfer Module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images, and an adversarial training ...
In this work, we conduct self-supervised training on a super-resolution model for medical images and smartly transfer the detailed representation knowledge in the trained super-resolution model to the ...
arXiv:2007.14556v3
fatcat:w4y7eltr7bfllcj7tmxovqmxki
MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation
[article]
2020
arXiv
pre-print
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Although convolutional neural networks (CNNs) are promoting the development of medical image semantic segmentation, the standard model still has some shortcomings. ...
We choose the best models in the medical image segmentation task for comparison, namely Attention U-net, CE-Net, UNet++, Unet and Channel-UNet. ...
The dense hole convolution block captures broader and deeper semantic features by injecting four cascaded branches with multi-scale hole convolution, and uses shortcut connections to prevent the problem ...
arXiv:2012.10952v1
fatcat:6mwkaoa55jevtk5p4k3vf5on24
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
[article]
2020
arXiv
pre-print
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They gauge the state-of-the-art in single image super-resolution. ...
The challenge task was to super-resolve an input image with a magnification factor 16 based on a set of prior examples of low and corresponding high resolution images. ...
Extreme Super-Resolution Members: Younghyun Jo 1 (yh.jo@yonsei.ac.kr), Sejong Yang 1 , Seon Joo Kim 1,2 Affiliation: 1 Yonsei University 2 Facebook Title: Cascade SR-GAN for Extreme Super-Resolution Members ...
arXiv:2005.01056v1
fatcat:6nwj5ilbgbgjnmd6oy435hjdhi
Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution
2019
IEEE Access
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Progressive up-sampling convolution gradually reconstructs a high-resolution feature map by taking into account short-and long-range dependencies. ...
INDEX TERMS Cell counting, human embryonic cells, IVF, medical image analysis, deep learning. ...
[38] came up with an interesting idea (sub-pixel convolution) to recover resolution in a single-image super-resolution scenarios. ...
doi:10.1109/access.2019.2920933
fatcat:w7a6kk7yqnfgpo6zribi7fpuvi
MR image reconstruction using deep learning: evaluation of network structure and loss functions
2019
Quantitative Imaging in Medicine and Surgery
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CNN image reconstruction using Resnet yields comparable image quality to Unet with 10X the number of parameters. This has implications for training with significantly lower data requirements. ...
Based on our assessments, we find that Resnet and Unet achieve similar image quality but that former requires only 100,000 parameters compared to 1.3 million parameters for the latter. ...
Deep learning-based approaches are well-developed in computer vision tasks such as image super-resolution (5) (6) (7) (8) , denoising and inpainting (9) (10) (11) (12) , while their application to medical ...
doi:10.21037/qims.2019.08.10
pmid:31667138
pmcid:PMC6785508
fatcat:iofvymidenakpgiujovgubkdse
Rethinking Coarse-to-Fine Approach in Single Image Deblurring
[article]
2021
arXiv
pre-print
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Second, the single decoder of the MIMO-UNet outputs multiple deblurred images with different scales to mimic multi-cascaded U-nets using a single U-shaped network. ...
Source code is available for research purposes at https://github.com/chosj95/MIMO-UNet. ...
The use of multi-scale images as an input for a single U-Net has also proven to be effective in other tasks such as depth map super-resolution [6] and object detection [21] . ...
arXiv:2108.05054v2
fatcat:pt5plw5q6rhvvhnkrzuikyxtjm
Woodland Labeling in Chenzhou, China, via Deep Learning Approach
2020
International Journal of Computational Intelligence Systems
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Secondly, this paper proposed a dense fully convolutional network (DFCN) which combines dense network with FCN model and achieves good semantic segmentation effect. ...
DFCN method is used to label the woodland in Gaofen-2 (GF-2) remote sensing images in Chenzhou. ...
There is also a lightweight network for image segmentation and classification [27] . In addition, the semantic segmentation also has similarities with image super-resolution reconstruction [28] . ...
doi:10.2991/ijcis.d.200910.001
doaj:a5d94b09bfc44d20af9dc9215ba39bb8
fatcat:mxpg5wto65ff7jtjlhkgzxny4i
Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception
2022
Remote Sensing
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This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original ...
The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. ...
for super-resolution remote sensing images. ...
doi:10.3390/rs14051118
fatcat:a3w3pjsrofb5pfj5bhyl3nglje
A Deep Journey into Super-resolution: A survey
[article]
2020
arXiv
pre-print
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single image super-resolution. ...
We introduce a taxonomy for deep-learning based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based ...
Densely Connected Networks Inspired by the success of the DenseNet [60] architecture for image classification, super-resolution algorithms based on densely connected CNN layers have been proposed to ...
arXiv:1904.07523v3
fatcat:ovihxjadfja55hrytvhggj5c6q
Accurate tissue interface segmentation via adversarial pre-segmentation of anterior segment OCT images
2019
Biomedical Optics Express
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Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. ...
To the best of our knowledge, this is the first work to show the potential of incorporating a cGAN into larger deep learning frameworks for improved corneal and limbal OCT image segmentation. ...
Acknowledgements We would like to thank the Center for Machine Learning in Health (CMLH) at Carnegie Mellon University for providing a fellowship to TSM. ...
doi:10.1364/boe.10.005291
pmid:31646047
pmcid:PMC6788614
fatcat:rdohuctrsbhkjh2hu2fasjfie4
An Improved Boundary-Aware Perceptual Loss for Building Extraction from VHR Images
2020
Remote Sensing
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(VHR) remote sensing images. ...
However, searching for better CNN architectures is time-consuming, and the robustness of a new CNN model cannot be guaranteed. ...
Acknowledgments: We thank Inria for providing the Inria Aerial Image Labelling Dataset and the WHU Aerial Dataset on their website (https://project.inria.fr/aerialimagelabelling/, http://study.rsgis.whu.edu.cn ...
doi:10.3390/rs12071195
fatcat:k4t6rdutq5eqthotvyv3wdrx3e
Guided Depth Map Super-Resolution using Recumbent Y Network
2020
IEEE Access
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INDEX TERMS Depth map super-resolution, convolutional neural network, UNet network, atrous spatial pyramid pooling, attention mechanism. ...
Depth map super-resolution (SR) can be used to enhance the resolution and improve the quality of depth maps. ...
In order to solve this issue, depth map super-resolution (SR) is developed to enhance the resolution of depth maps. ...
doi:10.1109/access.2020.3007667
fatcat:2ncqv3rbxncwhesslnzyeylyva
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