计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 277-283.doi: 10.11896/j.issn.1002-137X.2019.09.042
石晓红1,2,3, 黄钦开4, 苗佳欣5, 苏卓4,5
SHI Xiao-hong1,2,3, HUANG Qin-kai4, MIAO Jia-xin5, SU Zhuo4,5
摘要: 边缘保持滤波是计算机视觉、图像处理领域的重要基础理论研究,作为图像预处理操作对后续的处理结果有着重要影响。区别于传统滤波方法,边缘保持滤波方法不仅注重图像的平滑处理,还注重保持边缘细节。卷积神经网络在很多领域得到了应用,并取得显著的成果。本文将卷积神经网络引入边缘保持滤波,利用卷积神经网络的良好扩展性和灵活性来构建深度卷积神经网络模型(Deep Convolutional Neural Network,DCNN),通过3种类型的网络堆叠层,采用反向传播迭代更新网络参数,训练残差图像,实现基于DCNN的边缘保持滤波方法;还构建了基于梯度域的卷积神经网络模型(Gradient CNN,GCNN),对彩色图像的梯度信息进行学习,通过三层卷积对梯度图进行边缘保持平滑操作,得到边缘保持平滑梯度图,进而利用输入图像引导平滑梯度图进行彩色重建,得到彩色滤波图像。最后通过实验与常见的边缘保持滤波方法进行主观和客观评价对比。DCNN不仅在视觉上达到了其他滤波的效果,同时在处理时间上也存在较大优势,表明DCNN可以通过大量的数据训练有效地拟合出多种边缘保持滤波算法。与其他边缘保持滤波结果相比,GCNN在视觉上可以保持颜色风格与输入图像整体一致,而且图像相似度评价指标也更好,表明GCNN解决了部分滤波处理出现颜色偏差、梯度反转等问题,而且提高了处理效率。
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