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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2203-2209

Defocus deblurring algorithm based on deep recurrent neural network

Cheng Wentao1
Ren Dongwei2
Wang Qilong1
1. College of Intelligence & Computing, Tianjin University, Tianjin 300350, China
2. School of Computer Science & Technology, Harbin Institute of Technology, Harbin 150001, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0635

Abstract

Recent years, the motion deblurring algorithm based on deep learning has been widely concerned, while single defocus image deblurring is rarely studied. In order to specifically solve the defocus blur problem of single image, this paper proposed a defocus deblurring algorithm based on deep recurrent neural network. Firstly, the algorithm used two cascaded residual networks to estimate the defocus map and image deblurring, respectively. After that, to ensure that the depth features of defocus map and clear images could better propagate across stages and interact within stages, the algorithm introduced LSTM(long short-term memory) as a recurrent layer in the residual network. Finally, the whole residual network underwent several iterations and reused the network parameters during the iterative stages. To train the network, this paper produced a synthetic defocus blur image dataset, where each defocus blurred image contained a corresponding clear image and defocus map. The experimental results show that, compared with existing defocus deblurring methods, the proposed algorithm has significant advantages in both the subjective and objective image quality evaluation, and can produce sharper edges and clear details in the restoration results. On the real defocus blur dual-pixel image dataset DPD, the proposed algorithm improves the peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) by 0.77 dB and 5.6%, respectively, compared with DPDNet-Single. Therefore, the proposed method can effectively deal with defocus blur in real scenes.

Foundation Support

国家自然科学基金资助项目(62172127,61801326)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0635
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Technology of Graphic & Image
Pages: 2203-2209
Serial Number: 1001-3695(2022)07-046-2203-07

Publish History

[2022-01-25] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

程文涛, 任冬伟, 王旗龙. 基于循环神经网络的散焦图像去模糊算法 [J]. 计算机应用研究, 2022, 39 (7): 2203-2209. (Cheng Wentao, Ren Dongwei, Wang Qilong. Defocus deblurring algorithm based on deep recurrent neural network [J]. Application Research of Computers, 2022, 39 (7): 2203-2209. )

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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