Journal of Information Processing Systems

  • 제19권3호
  • /
  • Pages.370-376
  • /
  • 2023
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

Adaptive Enhancement Method for Robot Sequence Motion Images

  • Yu Zhang (College of Software Technology, Henan Finance University) ;
  • Guan Yang (School of Computer Science, Zhongyuan University of Technology)
  • 투고 : 2022.07.01
  • 심사 : 2022.08.28
  • 발행 : 2023.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Aiming at the problems of low image enhancement accuracy, long enhancement time and poor image quality in the traditional robot sequence motion image enhancement methods, an adaptive enhancement method for robot sequence motion image is proposed. The feature representation of the image was obtained by Karhunen-Loeve (K-L) transformation, and the nonlinear relationship between the robot joint angle and the image feature was established. The trajectory planning was carried out in the robot joint space to generate the robot sequence motion image, and an adaptive homomorphic filter was constructed to process the noise of the robot sequence motion image. According to the noise processing results, the brightness of robot sequence motion image was enhanced by using the multi-scale Retinex algorithm. The simulation results showed that the proposed method had higher accuracy and consumed shorter time for enhancement of robot sequence motion images. The simulation results showed that the image enhancement accuracy of the proposed method could reach 100%. The proposed method has important research significance and economic value in intelligent monitoring, automatic driving, and military fields.

키워드

참고문헌

  1. H. Yang, and Y. Wang, "Edge recognition adaptive recognition simulation of motion video images," Computer Simulation, vol. 36, no. 7, pp. 389-392, 2019.
  2. Z. Zhang, Q. Sun, X. Lin, and M. Han, "Image enhancement for space object based on information between adjacent spatial-temporal frames," Infrared and Laser Engineering, vol. 48, no. S1, pp. 193-197, 2019.
  3. Y. Ouyang, C. Deng, and L. Lin, "Motion image deblurring based on adaptive residuals," Computer Engineering and Design, vol. 42, no. 6, pp. 1684-1690,
  4. H. Wei and J. Wang, "Simulation research on edge sharpening enhancement of motion blurred digital image," Computer Simulation, vol. 37, no. 7, pp. 459-462+497,
  5. W. Yu and M. Zhang, "Simulation of image enhancement method for video surveillance image target motion track," Computer Simulation, vol. 36, no. 12, pp. 141-144+158, 2019.
  6. Z. Wang, G. Lv, M. Xu, Q. Feng, A. Wang, and H. Ming, "Resolution enhancement of spherical wave-based holographic stereogram with large depth range," Applied Sciences, vol. 11, no. 12, article no. 5595, 2021. https://doi.org/10.3390/app11125595
  7. L. Zhu, S. Chen, Q. Ma, S. Zhang, H. Zhao, and D. Wei, "Rotating micro-doppler parameter estimation of ground wheeled vehicles based on SPWD and image enhancement," Optik, vol. 219, article no. 165119, 2020. https://doi.org/10.1016/j.ijleo.2020.165119
  8. C. Zheng and M. Pang, "Spatial-temporal weight attitude motion feature extraction algorithm using convolutional neural network," Journal of Applied Sciences, vol. 39, no. 4, pp. 594-604, 2021.
  9. M. Lei, Y. Ping, X. Bing, and L. Yong, "Spatial resolution enhancement of planar compound eye based on variational Bayesian multi-image super-resolution," Opto-Electronic Engineering, vol. 47, no. 2, article no. 180661, 2020. https://doi.org/10.12086/oee.2020.180661
  10. X. Cheng, H. Pan, and J. Wang, "Research on Retinex enhancement of moving target in mixed multi range image," Computer Simulation, vol. 38, no. 12, pp. 105-108+290, 2021.