sis 18: e34

Research Article

Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement

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  • @ARTICLE{10.4108/eai.5-1-2022.172782,
        author={Xi Zhang and Jiyue Wang},
        title={Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={1},
        keywords={robot infrared image enhancement, Retinex method, contourlet-based non-local mean, Histogram equalization},
        doi={10.4108/eai.5-1-2022.172782}
    }
    
  • Xi Zhang
    Jiyue Wang
    Year: 2022
    Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement
    SIS
    EAI
    DOI: 10.4108/eai.5-1-2022.172782
Xi Zhang1, Jiyue Wang1,*
  • 1: School of Mechanical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China
*Contact email: wjiyue@126.com

Abstract

Aiming at the problems of fuzzy details and excessive enhancement in traditional robot infrared image enhancement algorithms, a robot infrared image enhancement method based on Retinex theory and contourlet-based non-local mean is proposed. Firstly, the single-scale Retinex method is used to adjust the gray level of the over-dark and over-bright parts of the image. Then, the contourlet-based non-local mean is used to decompose the image to obtain the basic layer and detail layer. Histogram equalization is used to stretch the contrast of the basic layer, and nonlinear function is used to enhance the detail layer. Finally, the results of different levels are fused to obtain the contrast and detail enhanced robot infrared image. The proposed method is used to simulate several groups of robot infrared images in different scenes, and compared with other enhancement methods for subjective and objective analysis. The results show that the proposed method achieves better performance in detail and contrast enhancement of infrared images.