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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2224-2229

Blind denoising model based on unsupervised deep image generation

Chen Xiaojun
Li Fen
Xu Shaoping
Xiao Nan
Cheng Xiaohui
School of Mathematics & Computer Sciences, Nanchang University, Nanchang 330031, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0601

Abstract

In view of the data dependence that is the major drawback of the supervised deep neural network(DNN) -based denoising models, this paper proposed an unsupervised deep image generation(UDIG) denoising model. Firstly, it utilized a noise level estimation(NLE) algorithm to measure the noise level of a given noisy image, the estimated noise level and the noisy image fed into the state-of-the-art denoising model(i. e., fast and flexible denoising convolutional neural network, FFDNet) to obtain a preliminary denoised image as the input to the UDIG model. Secondly, it chose the encoder-decoder architecture as backbone network, while using the sum of the mean square error among the output image of UDIG model(i. e. generated image), and the preliminary denoised image, and the given noisy image to define mixed loss function. Then, the regular stochastic gradient descent(SGD) algorithm optimized the loss function, adjusted the hyper-parameters of the UDIG model to generating a series of generated images. Finally, when the standard deviation of the residual image between the noisy image and generated image approximates the noise level measured by the NLE algorithm, the network iterative process was terminated adaptively, ensuring the image quality of the generated image(treated as the denoised image). Extensive experiments show that, the proposed UDIG denoising model has a better performance than other state-of-the-art counterparts with regard to denoising effect.

Foundation Support

国家自然科学基金资助项目(62162043,61662044,62162042)
江西省研究生创新专项资助项目(YC2021-S145)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0601
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Technology of Graphic & Image
Pages: 2224-2229
Serial Number: 1001-3695(2022)07-049-2224-06

Publish History

[2022-01-12] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

陈晓军, 李芬, 徐少平, 等. 基于无监督深度图像生成的盲降噪模型 [J]. 计算机应用研究, 2022, 39 (7): 2224-2229. (Chen Xiaojun, Li Fen, Xu Shaoping, et al. Blind denoising model based on unsupervised deep image generation [J]. Application Research of Computers, 2022, 39 (7): 2224-2229. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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