scholarly journals A new blind image denoising method based on asymmetric generative adversarial network

2021 ◽  
Vol 15 (6) ◽  
pp. 1260-1272
Author(s):  
Yiming Wang ◽  
Dongxia Chang ◽  
Yao Zhao
Keyword(s):  
Image Denoising ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Adversarial Network ◽  
Blind Image

scholarly journals An Image Denoising Method Based on BM4D and GAN in 3D Shearlet Domain

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shengnan Zhang ◽  
Lei Wang ◽  
Chunhong Chang ◽  
Cong Liu ◽  
Longbo Zhang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Evaluation Criteria ◽  
Structural Similarity ◽  
Block Matching ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Edge Preserving ◽  
Block Matching Algorithm ◽  
Adversarial Network

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuqin Li ◽  
Ke Zhang ◽  
Weili Shi ◽  
Yu Miao ◽  
Zhengang Jiang
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Superior Performance ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Automatic Feature Extraction ◽  
Adversarial Network ◽  
Image Context ◽  
Conditional Information ◽  
Denoised Image

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 existing methods for medical image denoising adapted to certain types of noise have difficulties in handling spatially varying noise; meanwhile, image detail losses and structure changes occurred in the denoised image. Considering image context perception and structure preserving, this paper firstly introduces a medical image denoising method based on conditional generative adversarial network (CGAN) for various unknown noises. In the proposed architecture, noise image with the corresponding gradient image is merged as network conditional information, which enhances the contrast between the original signal and noise according to the structural specificity. A novel generator with residual dense blocks makes full use of the relationship among convolutional layers to explore image context. Furthermore, the reconstruction loss and WGAN loss are combined as the objective loss function to ensure the consistency of denoised image and real image. A series of experiments for medical image denoising are conducted with the denoising results of PSNR = 33.2642 and SSIM = 0.9206 on JSRT datasets and PSNR = 35.1086 and SSIM = 0.9328 on LIDC datasets. Compared with the state-of-the-art methods, the superior performance of the proposed method is outstanding.

scholarly journals Unpaired Image Denoising Using a Generative Adversarial Network in X-Ray CT

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110414-110425 ◽  
Author(s):  
Hyoung Suk Park ◽  
Jineon Baek ◽  
Sun Kyoung You ◽  
Jae Kyu Choi ◽  
Jin Keun Seo
Keyword(s):  
Image Denoising ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Adversarial Network

scholarly journals Digital radiography image denoising using a generative adversarial network

2018 ◽  
Vol 26 (4) ◽  
pp. 523-534 ◽  
Author(s):  
Yuewen Sun ◽  
Ximing Liu ◽  
Peng Cong ◽  
Litao Li ◽  
Zhongwei Zhao
Keyword(s):  
Image Denoising ◽  
Digital Radiography ◽  
Generative Adversarial Network ◽  
Adversarial Network
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