Near-infrared vascular images play an important role in the diagnosis and treatment of vascular diseases. However, near-infrared vascular images often have problems such as low image quality and unclear vascular patterns. To solve these problems, we propose a Deep Convolutional Neural Network (DCNN) auto-encoder for image enhancement to enhance vascular structures and suppress non-vascular structures. We also collect a datasets of 156 images for the training and validation testing of the model; and further we use the full-reference image quality assessment metrics, i.e., Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) to quantitatively evaluate the image enhancement effect of this model. The experimental results show: compared with the traditional image enhancement algorithm, the enhanced image quality of the Residual Convolutional Auto-Encoder (RCAE) model is better and more similar to the original image.


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    Titel :

    Near-Infrared Vascular Image Enhancement Using Deep Convolutional Neural Network


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Long, Shengzhao (Herausgeber:in) / Dhillon, Balbir S. (Herausgeber:in) / Li, Yajie (Autor:in) / Liu, Haoting (Autor:in) / Wang, Yuan (Autor:in)

    Kongress:

    International Conference on Man-Machine-Environment System Engineering ; 2022 ; Beijing, China October 21, 2022 - October 23, 2022



    Erscheinungsdatum :

    2022-08-21


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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