对象引导的单幅散焦图像深度提取方法

doi:10.3969/j.issn.0372-2112.2014.10.022

电子学报 ›› 2014, Vol. 42 ›› Issue (10): 2009-2015.DOI: 10.3969/j.issn.0372-2112.2014.10.022

对象引导的单幅散焦图像深度提取方法

袁红星, 吴少群, 安鹏, 郑悠, 徐力

宁波工程学院电子与信息工程学院, 浙江宁波 315016

收稿日期:2013-07-15 修回日期:2014-03-25 出版日期:2014-10-25
- 作者简介:
- 袁红星 男.1980年10月出生,安徽安庆人.2010年毕业于中国科学技术大学电子科学与技术系,其后在美满电子科技有限公司从事信号处理理论与技术研究工作,2011进入宁波工程学院电子与信息工程学院,主要从事图像与信号处理、3D视频处理、2D转3D等方面的研究工作. E-mail:yuanhx@mail.ustc.edu.cn;吴少群 女.1981年12月出生,安徽安庆人.2011年毕业于华东师范大学职成教研究所,获教育学硕士学位.现为宁波工程学院讲师,主要从事图像与信号处理、3D视频处理等方面的研究工作. E-mail:vison101@163.com
- 基金资助:
- 国家自然科学基金 (No.61071173); 浙江省自然科学基金 (No.LY12F01001,No.LQ12D01001,No.LQ12F03001); 宁波市自然科学基金 (No.2012A610043,No.2012A610048,No.2011A610186); 浙江省教育厅科研项目 (No.Y201431834)

Object Guided Depth Map Recovery from a Single Defocused Image

YUAN Hong-xing, WU Shao-qun, AN Peng, ZHENG You, XU Li

School of Electronics and Information Engineering, Ningbo University of Technology, Ningbo, Zhejiang 315016, China

Received:2013-07-15 Revised:2014-03-25 Online:2014-10-25 Published:2014-10-25
- Supported by:
- National Natural Science Foundation of China (No.61071173); National Natural Science Foundation of Zhejiang Province, China (No.LY12F01001, No.LQ12D01001, No.LQ12F03001); Ningbo Natural Science Fund (No.2012A610043, No.2012A610048, No.2011A610186); Scientific Research Project of Zhejiang Education Department (No.Y201431834)

摘要/Abstract

摘要：

2D图像转3D图像是解决3D影视内容缺乏的主要手段之一,而深度提取是其中的关键步骤.考虑到影视作品中存在大量散焦图像,提出单幅散焦图像深度估计的方法:首先通过高斯卷积将散焦图像转换成两幅模糊程度不同的图像;其次计算这两幅图像在边缘处的梯度幅值比例,进而根据阶跃信号与镜头的卷积模型得到边缘处的模糊度;再次将边缘处的模糊度转换成图像的稀疏深度并利用拉普拉斯矩阵插值得到稠密深度图;最后通过图像的视觉显著度提取前景对象,建立对象引导的深度图优化能量模型,使前景的深度趋于一致并平滑梯度较小区域的深度.该方法利用对象引导的深度优化,剔除了拉普拉斯矩阵插值引入深度图的纹理信息.模拟图像的峰值信噪比和真实图像的视觉对比均表明该算法比现有方法有较大改善.

关键词: 2D转3D, 散焦图, 深度图, 视觉显著度, 深度图优化

Abstract:

2D-to-3D conversion is a feasible solution to the problem of 3D-content deficiency.In the conversion, depth extraction from a single 2D image is the key step.We propose a depth estimation method based on edge-gradients ratio and object-guided energy model.First, we obtain two blurred images from the input defocused image via Gaussian smoothing using two different kernels.Then, we estimate the sparse depth map generated from the gradients ratio at edge locations in the two blurred images.Next, we recover the full depth map from the sparse depth map by matting Laplacian interpolation.Objects are then extracted from the input image by adaptive threshold binary segmentation on its visual saliency map.Finally, the refined depth map is obtained through object-guided depth filtering.Synthetic and real images experimental results both show that our algorithm is superior to the existing methods.

Key words: 2D-to-3D, defocused image, depth map, visual saliency, depth refinement

中图分类号:

TN911.73

袁红星, 吴少群, 安鹏, 等. 对象引导的单幅散焦图像深度提取方法[J]. 电子学报, 2014, 42(10): 2009-2015.

YUAN Hong-xing, WU Shao-qun, AN Peng, et al. Object Guided Depth Map Recovery from a Single Defocused Image[J]. Acta Electronica Sinica, 2014, 42(10): 2009-2015.

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对象引导的单幅散焦图像深度提取方法

Object Guided Depth Map Recovery from a Single Defocused Image

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