对象引导的单幅散焦图像深度提取方法

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.

Object Guided Depth Map Recovery from a Single Defocused Image

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.