Adaptive Enhancement Method for Robot Sequence Motion Images


Yu Zhang, Guan Yang, Journal of Information Processing Systems Vol. 19, No. 3, pp. 370-376, Jun. 2023  

10.3745/JIPS.02.0196
Keywords: Adaptive Enhancement, K-L Transformation, Multiscale Retinex Algorithm, robot, Sequence Motion Image
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Abstract

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.


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Cite this article
[APA Style]
Zhang, Y. & Yang, G. (2023). Adaptive Enhancement Method for Robot Sequence Motion Images. Journal of Information Processing Systems, 19(3), 370-376. DOI: 10.3745/JIPS.02.0196.

[IEEE Style]
Y. Zhang and G. Yang, "Adaptive Enhancement Method for Robot Sequence Motion Images," Journal of Information Processing Systems, vol. 19, no. 3, pp. 370-376, 2023. DOI: 10.3745/JIPS.02.0196.

[ACM Style]
Yu Zhang and Guan Yang. 2023. Adaptive Enhancement Method for Robot Sequence Motion Images. Journal of Information Processing Systems, 19, 3, (2023), 370-376. DOI: 10.3745/JIPS.02.0196.