Image Denoising Based on GAN with Optimization Algorithm
文献类型：期刊论文
| 作者 | Zhu, Min-Ling2; Zhao, Liang-Liang2; Xiao, Li1,3 |
| 刊名 | ELECTRONICS
 |
| 出版日期 | 2022-08-01 |
| 卷号 | 11期号:15页码:12 |
| 关键词 | image denoising GAN optimization algorithm autoencoder ResNet |
| DOI | 10.3390/electronics11152445 |
| 英文摘要 | 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. Denoising networks based on deep learning technology still face some problems, such as in their accuracy and robustness. This paper constructs a robust denoising network based on a generative adversarial network (GAN). Since the neural network has the phenomena of gradient dispersion and feature disappearance, the global residual is added to the autoencoder in the generator network, to extract and learn the features of the input image, so as to ensure the stability of the network. On this basis, we proposed an optimization algorithm (OA), to train and optimize the mean and variance of noise on each node of the generator. Then the robustness of the denoising network was improved through back propagation. Experimental results showed that the model's denoising effect is remarkable. The accuracy of the proposed model was over 99% in the MNIST data set and over 90% in the CIFAR10 data set. The peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of the proposed model were better than the state-of-the-art models in the BDS500 data set. Moreover, an anti-interference test of the model showed that the defense capacities of both the fast gradient sign method (FGSM) and project gradient descent (PGD) attacks were significantly improved, with PSNR and SSIM values decreased by less than 2%. |
| 资助项目 | Beijing Natural Science Foundation[4202025] ; National Natural Science Foundation of China[31900979] ; Promoting the classified development of colleges and universities-the construction of the first level discipline of Computer Science and Technology[5112211036] |
| WOS研究方向 | Computer Science ; Engineering ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000839121100001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19458]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Xiao, Li |
| 作者单位 | 1.Univ Chinese Acad Sci, Ningbo Huamei Hosp, Ningbo 315010, Peoples R China 2.Beijing Informat Sci & Technol Univ, Comp Sch, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100090, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Min-Ling,Zhao, Liang-Liang,Xiao, Li. Image Denoising Based on GAN with Optimization Algorithm[J]. ELECTRONICS,2022,11(15):12. |
| APA | Zhu, Min-Ling,Zhao, Liang-Liang,&Xiao, Li.(2022).Image Denoising Based on GAN with Optimization Algorithm.ELECTRONICS,11(15),12. |
| MLA | Zhu, Min-Ling,et al."Image Denoising Based on GAN with Optimization Algorithm".ELECTRONICS 11.15(2022):12. |
入库方式： OAI收割
来源：计算技术研究所
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