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Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising
[article]
2018
arXiv
pre-print
Preserved Fulltext
To circumvent this issue, we leverage recently proposed approach of [7] that incorporates Stein's Unbiased Risk Estimator (SURE) to train a deep convolutional neural network without requiring denoised ...
Our experimental results demonstrate the effectiveness of SURE based approach for denoising X-ray images. ...
Acknowledgement We gratefully acknowledge the support of the NVIDIA Corporation for the donation of NVIDIA TITAN Xp GPU for our research. ...
arXiv:1811.12488v1
fatcat:upvrhwafe5eb7cxtv5fosw5hwm
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal
[article]
2022
arXiv
pre-print
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Additionally, another convolutional neural network is used to conduct the denoising and restoration work. ...
There are several previous methods based on neural network can have great performance in denoising salt and pepper noise. ...
The main idea of the first step is to design a convolutional neural network for detecting the position of noise pixels and separate it from the clean part of image. ...
arXiv:2206.05520v2
fatcat:veybgmbsvrcwpis7rshhq2utei
Real-Time Medical Video Denoising with Deep Learning: Application to Angiography
2018
International Journal of Applied Information Systems
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This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. ...
The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. ...
This work presents a combination of a convolutional neural network architecture with an autoencoding architecture to create a hybrid architecture which is specialized for denoising of image data. ...
doi:10.5120/ijais2018451755
pmid:29877510
pmcid:PMC5985814
fatcat:2yylttimtzdipputad3z4owv44
Deep Encoder-Decoder Neural Network for Fingerprint Image Denoising and Inpainting
[article]
2020
arXiv
pre-print
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while using the dilated convolution in the network to increase the receptor field without increasing the complexity and improve the network inference speed. ...
Fingerprint image denoising is a very important step in fingerprint identification. to improve the denoising effect of fingerprint image,we have designs a fingerprint denoising algorithm based on deep ...
[6] proposed the problem of denoising natural images using convolutional neural networks, looking at the denoising process as a neural network fitting process to improve the signal-to-noise ratio of ...
arXiv:2005.01115v1
fatcat:annnfgf5bfbmna5xkopmuezu4y
Implementation of Image Denoising Using Deep Neural Network
2020
Zenodo
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First, we divide images into four groups based on the type of convolutional neural network (CNN) they were processed through: CNNs trained on incremental white noise, CNNs trained on true noise, CNNs trained ...
Numerous researchers have looked into the potential of deep learning methods for use in image denoising. ...
To begin, we can categorise deep convolutional neural networks (CNNs) as either being used for processing additive white noisy images, real noisy images, blind denoising, or hybrid noisy images that incorporate ...
doi:10.5281/zenodo.7550192
fatcat:75ihvuy66zb47e4b3kd7zisz7q
Semi-Supervised Learning-Based Image Denoising for Big Data
2020
IEEE Access
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Through the research in this article, it is verified that the improved convolutional neural network denoising model and multi-feature extraction technology have strong advantages in image denoising. ...
Semi-supervised residual learning based on convolutional network is a good image denoising and denoising network model. Compared with other excellent denoising algorithms, it has very good results. ...
The same is true for convolutional neural networks to learn the basic features of an image. The actual process is to give the input image and scan the image using the convolutional kernel. ...
doi:10.1109/access.2020.3025324
fatcat:tumqijargbgovpdmwo6t5r4yn4
Medical Image Enhancement Based on Convolutional Denoising Autoencoders and GMD Model
2021
Modern Machine Learning Technologies
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In this paper, we demonstrate the using of Convolutional Denoising Autoencoders (CDAE) to enhance the images we obtained from DCGAN (which we obtained from previous paper). ...
There are many methods have been developed for this purpose. ...
Recently, Convolutional neural networks (CNNs) are widely uses in medical image classification [1] . ...
dblp:conf/momlet/YaroshchakSB21
fatcat:e4ll65dl3ve3bivzih3cfavadm
Convolutional neural networks for improving image quality with noisy PET data
2020
EJNMMI Research
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by a 3D convolution neural network. ...
A wide range of controlled noise levels was emulated from a set of chest PET data in patients with lung cancer, and a convolutional neural network was trained to denoise the reconstructed images using ...
Methods
Convolutional neural network A convolutional neural network framework, compatible for 3D data, was developed in C++ and built on the CUDA deep learning libraries. ...
doi:10.1186/s13550-020-00695-1
pmid:32955669
pmcid:PMC7505915
fatcat:ij6qwom5pvb2fmeezua6m65lsm
Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images
2022
Journal of Imaging
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A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. ...
The advantages of the proposed neural network are the number reduction in the trainable parameters and the increase in the similarity between the denoised or deblurred image and the original one. ...
• Image denoising for Poisson noise with a dual autoencoder achieved better results for both the use of a convolutional neural network and separable convolutional neural network. ...
doi:10.3390/jimaging8090250
pmid:36135415
pmcid:PMC9502178
fatcat:tl2zpqnxezduhdazxi5kbhynou
Chaining Identity Mapping Modules for Image Denoising
[article]
2019
arXiv
pre-print
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2017 pre-print arXiv:1712.02933v1 fatcat:nc5osmc3m5ghrkn2o7m5lvtiyq
We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. ...
Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, in other words within an identity mapping module, each neuron in the last convolution layer can observe the full ...
To provide a solution, our choice is the convolutional neural networks in a discriminative prior setting for image denoising. ...
arXiv:1712.02933v2
fatcat:xc2zaybzbbcknhx6tf2utykhnu
Deep Signal-Dependent Denoising Noise Algorithm
2023
Electronics
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We use the noise level of the noise image and the noise image together as the input of the convolutional neural network to obtain a wider range of noise levels than the single noise image as the input. ...
In the convolutional neural network, the deep features of the image are extracted by multi-layer residuals, which solves the difficult problem of training. ...
[13] proposed a true wide CNN (WCNN) to reorganize several convolutional layers. ...
doi:10.3390/electronics12051201
fatcat:ll65nilmcrbvhkiqkuskipzhoq
Image Denoising Based on GAN with Optimization Algorithm
2022
Electronics
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Image denoising has been a knotty issue in the computer vision field, although the developing deep learning technology has brought remarkable improvements in image denoising. ...
of the input image, so as to ensure the stability of the network. ...
., for supporting the open-access publication of this paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics11152445
fatcat:65a52krsfjhmjkbwwkq4la33va
Classification of Brain Tumors Using Hybridized Convolutional Neural Network in Brain MRI images
2022
North atlantic university union: International Journal of Circuits, Systems and Signal Processing
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the MRI image is denoised using Anisotropic diffusion filter, then MRI image is segmented using Morphological operations, to classify the images for the disorder CNN based hybrid technique is incorporated ...
In this research article, we have proposed a novel technique to operate on the Magnetic Resonance Imaging (MRI) data images which can be classified as image classification, segmentation and image denoising ...
Hybridized Convolutional Neural Network is used for this Classification. ...
doi:10.46300/9106.2022.16.70
fatcat:g4i6gebhbnap5ddgcmwom5snqq
Application of Artificial Intelligence to Cardiovascular Computed Tomography
2021
Korean Journal of Radiology
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The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation. ...
Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial ...
The following four algorithm types have been used in the deep learning applications for cardiac CT: convolutional neural network (CNN), fully convolutional neural network (FCN), recurrent neural network ...
doi:10.3348/kjr.2020.1314
pmid:34402240
pmcid:PMC8484158
fatcat:33n4ufdbs5ho7cyev4ng7qrdca
Image classification for Automobile pipe joints surface defect detection using Wavelet decomposition and Convolutional neural network
2022
IEEE Access
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Finally, the multichannel fusion convolutional neural network of decision-level is used to identify the surface defect types. ...
neural network to identify the types of defects. ...
The matrix is used as the final classification criterion for image classification.
FIGURE 13. Model of convolutional neural network in decision-level fusion. ...
doi:10.1109/access.2022.3178380
fatcat:uycssvvy2bdjzi66kfbfn6rdjy
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