基于深度学习的图像去噪方法研究综述

Overview of Image Denoising Methods Based on Deep Learning

图像去噪是利用图像序列的上下文信息去除噪声,从而恢复出清晰图像的一种技术,是计算机视觉领域重要研究内容之一。随着机器学习的发展,深度学习在图像去噪领域得到广泛应用,成为处理图像去噪的有效解决方法。分析了深度学习图像去噪方法;依据网络结构详细分析了图像去噪方法的思想,并对优缺点进行梳理总结;通过在DND、PolyU等数据集上的实验结果,对比分析基于深度学习去噪方法的性能;对图像去噪研究的关键问题进行总结,并讨论该领域未来研究的发展趋势。

Image denoising is a kind of technology that uses the context information of image sequence to remove noise and restore clear image.It is one of the important research contents in the field of computer vision.With the development of machine learning,deep learning has been widely used in the field of image denoising,and has become an effective solution for image denoising.Firstly,the deep learning image denoising method is analyzed.Secondly,the idea of image denoising method is analyzed in detail according to the network structure,and the advantages and disadvantages are summarized.Then,through the experimental results on DND,PolyU and other data sets,the performance of deep learning based image denoising methods is compared and analyzed.Finally,the key issues of image denoising research are summarized,and the future development trend of the research of this field is discussed.