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A variational approach to recovering depth from defocused images

机译:一种从散焦图像恢复深度的变分方法

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In this paper, we propose a regularized solution to the depth from defocus (DFD) problem using the space-frequency representation (SFR) framework. A smoothness constraint is imposed on the estimates of the blur parameter, and a variational approach to the DFD problem is developed. Among the numerous SFRs, we study the applicability of the complex spectrogram and the Wigner distribution, in particular, for depth recovery. The performance of the proposed variational method is tested on both synthetic and real images. The method yields good results, and the quality of the estimates is significantly better than that obtained without the smoothness constraint on the blur parameter.
机译:在本文中,我们使用空间频率表示(SFR)框架提出了一种针对散焦深度(DFD)问题的正规化解决方案。将平滑性约束强加于模糊参数的估计上,并开发了针对DFD问题的变分方法。在众多SFR中,我们研究了复杂频谱图和Wigner分布的适用性,特别是对于深度恢复。所提出的变分方法的性能已在合成图像和真实图像上进行了测试。该方法产生了良好的结果,并且估计的质量明显好于没有对模糊参数进行平滑度约束的情况。

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