A Novel Medical Image Denoising Method Based on Conditional Generative Adversarial Network

A Novel Medical Image Denoising Method Based on Conditional Generative Adversarial Network

Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent ability of automatic feature extraction. Most exist...

Full description

Bibliographic Details
Main Authors:	Yuqin Li, Ke Zhang, Weili Shi, Yu Miao, Zhengang Jiang
Format:	Article
Language:	English
Published:	Hindawi Limited 2021-01-01
Series:	Computational and Mathematical Methods in Medicine
Online Access:	http://dx.doi.org/10.1155/2021/9974017

Similar Items

Boosting Magnetic Resonance Image Denoising With Generative Adversarial Networks
by: Miao Tian, et al.
Published: (2021-01-01)

Image Denoising of Printed Circuit Boards Using Conditional Generative Adversarial Network
by: Pedro Menendez, et al.
Published: (2019)

Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement
by: Songkui Chen, et al.
Published: (2020-01-01)

An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks
by: Ruihua Wang, et al.
Published: (2018-06-01)

An ECG Denoising Method Based on the Generative Adversarial Residual Network
by: Bingxin Xu, et al.
Published: (2021-01-01)

A new blind image denoising method based on asymmetric generative adversarial network
by: Yiming Wang, et al.
Published: (2021-05-01)

Adversarial Gaussian Denoiser for Multiple-Level Image Denoising
by: Aamir Khan, et al.
Published: (2021-04-01)

A Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses
by: Sung In Cho, et al.
Published: (2021-02-01)

Instance Map Based Image Synthesis With a Denoising Generative Adversarial Network
by: Ziqiang Zheng, et al.
Published: (2018-01-01)

Unpaired Image Denoising Using a Generative Adversarial Network in X-Ray CT
by: Hyoung Suk Park, et al.
Published: (2019-01-01)

High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising
by: Linlin Yang, et al.
Published: (2020-01-01)

Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information
by: Chao Tang, et al.
Published: (2019-01-01)

CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network
by: Binit Vasant Gajera, et al.
Published: (2021-01-01)

Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function
by: Zhihua Li, et al.
Published: (2021-01-01)

A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks
by: Pawel Burdziakowski
Published: (2020-08-01)

Constrained Generative Adversarial Networks
by: Xiaopeng Chao, et al.
Published: (2021-01-01)

Recurrent Conditional Generative Adversarial Network for Image Deblurring
by: Jing Liu, et al.
Published: (2019-01-01)

The Defense of Adversarial Example with Conditional Generative Adversarial Networks
by: Fangchao Yu, et al.
Published: (2020-01-01)

SAR image synthesis based on conditional generative adversarial networks
by: Jianyu Wang, et al.
Published: (2019-09-01)

Application of Generative Adversarial Networks to Image Inpainting
by: Jiang Jian Wei, et al.
Published: (2019)

MGMDcGAN: Medical Image Fusion Using Multi-Generator Multi-Discriminator Conditional Generative Adversarial Network
by: Jun Huang, et al.
Published: (2020-01-01)

A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks
by: Donghui Hu, et al.
Published: (2018-01-01)

COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network
by: Chen, H., et al.
Published: (2021)

Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning
by: Yinjin Ma, et al.
Published: (2020-01-01)

InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography
by: K. A. Saneera Hemantha Kulathilake, et al.
Published: (2021-01-01)

Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator
by: Jianning Chi, et al.
Published: (2020-01-01)

Image Denoising Using a Novel Deep Generative Network with Multiple Target Images and Adaptive Termination Condition
by: Shiming Chen, et al.
Published: (2021-05-01)

Image Inpainting Based on Generative Adversarial Networks
by: Yi Jiang, et al.
Published: (2020-01-01)

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification
by: Afifatul Mukaroh, et al.
Published: (2020-10-01)

Generation of Synthetic Images with Generative Adversarial Networks
by: Zeid Baker, Mousa
Published: (2018)

Medical Image Super Resolution Using Improved Generative Adversarial Networks
by: Xinyang Bing, et al.
Published: (2019-01-01)

Image Embedding into Generative Adversarial Networks
by: Abdal, Rameen
Published: (2020)

Conditional High-Resolution Image Synthesizing Based on Generative Adversarial Networks
by: Ting-Jia Yang, et al.
Published: (2018)

General Deep Image Completion with Lightweight Conditional Generative Adversarial Networks
by: Tseng, Ching-Wei, et al.
Published: (2017)

Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network
by: Rui-Qiang Ma, et al.
Published: (2020-01-01)

Multilevel Image Dehazing Algorithm Using Conditional Generative Adversarial Networks
by: Kailei Gan, et al.
Published: (2020-01-01)

Recent Advances of Image Steganography With Generative Adversarial Networks
by: Jia Liu, et al.
Published: (2020-01-01)

Improved Wasserstein conditional generative adversarial network speech enhancement
by: Shan Qin, et al.
Published: (2018-07-01)

Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks
by: Ching-Chun Chang, et al.
Published: (2021-01-01)

Multi-Source Medical Image Fusion Based on Wasserstein Generative Adversarial Networks
by: Zhiguang Yang, et al.
Published: (2019-01-01)