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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    Title :

    Near-Infrared Vascular Image Enhancement Using Deep Convolutional Neural Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:

    Conference:

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



    Publication date :

    2022-08-21


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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