首页> 外文期刊>IEEE Transactions on Image Processing >Deep Convolutional Neural Network for Natural Image Matting Using Initial Alpha Mattes
【24h】

Deep Convolutional Neural Network for Natural Image Matting Using Initial Alpha Mattes

机译:使用初始Alpha遮罩进行自然图像遮罩的深度卷积神经网络

获取原文
获取原文并翻译 | 示例

摘要

We propose a deep convolutional neural network (CNN) method for natural image matting. Our method takes multiple initial alpha mattes of the previous methods and normalized RGB color images as inputs, and directly learns an end-to-end mapping between the inputs and reconstructed alpha mattes. Among the various existing methods, we focus on using two simple methods as initial alpha mattes: the closed-form matting and KNN matting. They are complementary to each other in terms of local and nonlocal principles. A major benefit of our method is that it can “recognize” different local image structures and then combine the results of local (closed-form matting) and nonlocal (KNN matting) mattings effectively to achieve higher quality alpha mattes than both of the inputs. Furthermore, we verify extendability of the proposed network to different combinations of initial alpha mattes from more advanced techniques such as KL divergence matting and information-flow matting. On the top of deep CNN matting, we build an RGB guided JPEG artifacts removal network to handle JPEG block artifacts in alpha matting. Extensive experiments demonstrate that our proposed deep CNN matting produces visually and quantitatively high-quality alpha mattes. We perform deeper experiments including studies to evaluate the importance of balancing training data and to measure the effects of initial alpha mattes and also consider results from variant versions of the proposed network to analyze our proposed DCNN matting. In addition, our method achieved high ranking in the public alpha matting evaluation dataset in terms of the sum of absolute differences, mean squared errors, and gradient errors. Also, our RGB guided JPEG artifacts removal network restores the damaged alpha mattes from compressed images in JPEG format.
机译:我们提出了一种用于自然图像抠像的深度卷积神经网络(CNN)方法。我们的方法将先前方法的多个初始alpha遮罩和标准化的RGB彩色图像作为输入,并直接学习输入与重构的alpha遮罩之间的端到端映射。在现有的各种方法中,我们着重于使用两种简单的方法作为初始alpha遮罩:闭合形式的遮罩和KNN遮罩。它们在本地和非本地原则上是互补的。我们方法的主要优点是它可以“识别”不同的局部图像结构,然后有效地组合局部(闭合形式的抠图)和非局部(KNN抠图)的抠图结果,从而获得比两个输入都更高质量的alpha遮罩。此外,我们从更先进的技术(例如KL散度消光和信息流消光)验证了所提出的网络对初始alpha遮罩的不同组合的可扩展性。在深层CNN抠图的顶部,我们构建了RGB引导的JPEG伪像去除网络,以处理alpha抠像中的JPEG块伪像。大量实验表明,我们提出的深CNN遮罩可在视觉和数量上产生高质量的alpha遮罩。我们进行了更深入的实验,包括评估平衡训练数据的重要性并测量初始alpha遮罩的效果的研究,还考虑了拟议网络变体的结果来分析我们提出的DCNN遮罩。此外,在绝对差异,均方误差和梯度误差之和方面,我们的方法在公共alpha遮罩评估数据集中获得了很高的排名。此外,我们的RGB引导的JPEG伪影去除网络可从JPEG格式的压缩图像中恢复损坏的alpha遮罩。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号