基于L0梯度最小化的单幅图像深度恢复

Fengyun Cao, Fei Xie
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引用次数: 0

摘要

针对单幅图像深度恢复的难题,提出了一种基于L0梯度最小化的局部离焦模糊估计算法。现有方法存在一个共同的问题,即弱边缘、噪声或软阴影处的量化误差可能导致某些边缘位置的模糊估计不准确。在该方法中,采用L0平滑技术筛选有效边缘,有利于估计离焦信息和去噪。利用引导图像滤波器将边缘位置的模糊量传播到整个图像,得到精细的散焦图。然后从模糊图中恢复图像的相对深度顺序。最后,采用t形结消除了焦平面上深度图的模糊性。实验结果表明,与以往的方法相比,该算法可以有效地生成高质量的深度图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depth Recovery from a Single Image Based on L0 Gradient Minimization
Aiming at the challenging problem of single image depth recovery, a new local defocus blur estimation algorithm is presented based on L0 gradient minimization. There is a common problem in the existing methods, that is, quantization error at weak edges, noise or soft shadows may cause inaccurate blur estimates at some edge locations. In the proposed methods, L0 smoothing technology is employed to screen the effective edge which is advantageous to estimate defocus information and denoising. The guided image filter is applied to propagate the blur amount at edge locations to the entire image, a refined defocus map can be obtained. Then the recovery of the relative depth order on the image is achieved from the blur map. At last, T-junction is adopted to eliminate the ambiguity in the depth map over the focal plane. Experimental results demonstrate that compared with the previous approaches, the algorithm can effectively produces high quality depth maps.
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