基于U2Net的自动抠图技术研究

Research on Automatic Matting Technology based on U2Net

针对计算机视觉领域当中的抠图问题,提出了一种基于U2Net的自动抠图技术,以快速、准确地分离图像中的前景和背景。研究采用深度学习模型,通过学习图像中前景和背景的差异来实现高效、精准的抠图过程。在实验过程中,使用经过预训练的U2Net模型来进行抠图,采用均分误差等通用的抠图效果评价指标对结果进行评判,较其他抠图技术表现出更优的效果。实验表明,在各种复杂场景下,该技术展现出良好的抠图效果,并且具备更高的准确率和更快的处理速度。同时该技术在图像编辑、计算机视觉、医疗影像等多个领域都有广泛的应用前景。

Aiming at the problem of image matting in the field of computer vision,an automatic image matting technology is proposed in this paper based on U2Net,so as to quickly and accurately separate the foreground and background in the image.The deep learning model is adopted in this study to realize efficient and accurate matting process by learning the difference between foreground and background in images.During the experiment,the pre-trained U2Net model is used to matting,and the results are evaluated by general evaluation indexes such as equalization error.This method shows better results than other matting techniques.Experimental results show that the technique has good matting effect,higher accuracy and faster processing speed in various complex scenes.At the same time,this technology has a wide range of applications in image editing,computer vision,medical imaging and other fields.