Journal of Information Processing Systems

  • 제19권6호
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  • Pages.858-869
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  • 2023
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Multiple Mixed Modes: Single-Channel Blind Image Separation

  • Tiantian Yin (Electronics and Communication Engineering, Taiyuan University of Science and Technology) ;
  • Yina Guo (Electronics and Communication Engineering, Taiyuan University of Science and Technology) ;
  • Ningning Zhang (Upgrading Office of Modern College of Humanities and Sciences of Shanxi Normal University)
  • 투고 : 2022.06.16
  • 심사 : 2023.01.21
  • 발행 : 2023.12.31
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초록

As one of the pivotal techniques of image restoration, single-channel blind source separation (SCBSS) is capable of converting a visual-only image into multi-source images. However, image degradation often results from multiple mixing methods. Therefore, this paper introduces an innovative SCBSS algorithm to effectively separate source images from a composite image in various mixed modes. The cornerstone of this approach is a novel triple generative adversarial network (TriGAN), designed based on dual learning principles. The TriGAN redefines the discriminator's function to optimize the separation process. Extensive experiments have demonstrated the algorithm's capability to distinctly separate source images from a composite image in diverse mixed modes and to facilitate effective image restoration. The effectiveness of the proposed method is quantitatively supported by achieving an average peak signal-to-noise ratio exceeding 30 dB, and the average structural similarity index surpassing 0.95 across multiple datasets.

키워드

과제정보

This paper is funded by National Natural Science Foundation of China (Grant No. 61301250), China Scholarship Council (Grant No. [2020]1417), Key Research and Development Project of Shanxi Province (Grant No. 201803D421035), Natural Science Foundation for Young Scientists of Shanxi Province (Grant No. 201901D211313), Shanxi Scholarship Council of China (Grant No. HGKY2019080 and 2020-127), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (STIP) (Grant No. 2022L641), and Shanxi Province Postgraduate Excellent Innovation Project Plan (Grant No. 2021Y679).

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