App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 6

Deep neural network inpainting method based on optimized receptive field strategy

Liu Enze
Liu Huaming
Wang Xiuyou
Bi Xuehui
College of Computer & Information Engineering, Fuyang Normal University, Fuyang 236000, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0406

Abstract

The currently popular image inpainting methods based on deep neural networks typically employ large receptive field feature extractors. However, when restoring local patterns and textures, they often generate artifacts or distorted textures, thus failing to recover the overall semantic and visual structure of the image. To address this issue, a novel image inpainting method, known as Optimized Receptive Field (ORFNet) , is introduced, which combines coarse and fine inpainting by employing an optimized receptive field strategy. Initially, a coarse inpainting result is obtained using a generative adversarial network with a large receptive field. Subsequently, a model with a small receptive field is used to refine local texture details. Finally, a global refinement inpainting is performed using an encoder-decoder network based on attention mechanisms. Validation on the CelebA, Paris StreetView, and Places2 datasets demonstrates that ORFNet outperforms existing representative inpainting methods. It leads to a 1.98 dB increase in PSNR and a 2.49% improvement in SSIM, along with an average 2.4% reduction in LPIPS. Experimental results confirm the effectiveness of the proposed image inpainting method, showcasing superior performance across various receptive field settings and achieving a more realistic and natural visual outcome.

Foundation Support

安徽省高校自然科学研究重大项目(KJ2020ZD46)
阜阳师范大学高层次人才科研启动项目(2020KYQD0032)
阜阳师范大学校级项目(rcxm202001,2020FSKJ12,2021FSKJ01ZD)
阜阳市校合作项目(SXHZ202103)
阜阳师范大学阜阳市产业链研究创新团队(CYLTD202213)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0406
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 6

Publish History

[2023-11-15] Accepted Paper

Cite This Article

刘恩泽, 刘华明, 王秀友, 等. 基于优化感受野策略的图像修复方法 [J]. 计算机应用研究, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0406. (Liu Enze, Liu Huaming, Wang Xiuyou, et al. Deep neural network inpainting method based on optimized receptive field strategy [J]. Application Research of Computers, 2024, 41 (6). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0406. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)