基于光场结构特性与多视点匹配的深度估计

Depth estimation based on light field structure characteristic and multiview matching

  • 摘要: 针对现有光场图像深度估计技术无法均衡地对主要对象和背景进行深度估计的问题,提出了一种基于光场结构特性与多视点匹配的深度估计方法。该方法在光场结构特性引导的深度估计的基础上,为了实现光场图像深度变化区域的平滑过渡,同时又考虑光场图像具有多视点子孔径图像阵列的特点,采用多视点匹配优化光场图像深度估计。在马尔可夫随机域中,基于光场结构特性构建深度估计平滑项,同时联合多视点匹配构建深度估计数据项,并进行全局深度迭代优化,从而有效平衡对象深度边界和背景深度估计,提高光场图像深度估计的性能。实验结果表明,所提出的方法能够得到更加清晰的深度边界,同时可以修正背景中不准确的深度值,获得高质量的深度估计结果。

     

    Abstract: The existing light field image depth estimation technique cannot make a balanced estimation between major object and background. In this paper, a novel depth estimation method based on light field structure characteristic and multiview matching was proposed for light field image. The light field structure guided method was used as the basis of depth estimation. In order to maintain a smooth transition of the depth changing region and consider that the light field image has multiview subaperture image arrays, the multiview matching was presented to optimize the depth estimation. In the Markov random field domain, a smooth term was constructed based on the characteristics of the light field structure and a data term was constructed based on multiview stereo matching. Then, an optimization method utilizing the above two terms was proposed to balance object depth boundary and background depth estimation, so as to improve the depth estimation of light field images. Experimental results show that the proposed method can produce high quality depth estimation results with clear depth boundary and accurate depth in background.

     

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