Xiang Sen, Liu Li, Deng Huiping, Wu Jin, Yang You, Yu Li
Opt Express. 2021 Sep 13;29(19):30077-30093. doi: 10.1364/OE.434548.
Depth estimation is a fundamental task in light field (LF) related applications. However, conventional light field suffers from the lack of features, which introduces depth ambiguity and heavy computation load to depth estimation. In this paper, we introduce phase light field (PLF), which uses sinusoidal fringes as patterns and the latent phases as the codes. With PLF and the re-formatted phase-epipolar-plane-images (phase EPIs), a global cost minimization framework is proposed to estimate the depth. In general, EPI-based depth estimation tests a set of candidate lines to find the optimal one with most similar intensities, and the slope of the optimal line is converted to disparity and depth. Based on this principle, for phase-EPI, we propose a cost with weighted phase variance in the candidate line, and we prove that the cost is a convex function. After that, the beetle antennae search (BAS) optimization algorithm is utilized to find the optimal line and thus depth can be obtained. Finally, a bilateral filter is incorporated to further improve the depth quality. Simulation and real experimental results demonstrate that, the proposed method can produce accurate depth maps, especially at boundary regions. Moreover, the proposed method achieves an acceleration of about 5.9 times over the state-of-the-art refocus method with comparable depth quality, and thus can facilitate practical applications.
深度估计是光场（LF）相关应用中的一项基本任务。然而，传统光场存在特征不足的问题，这给深度估计带来了深度模糊性和繁重的计算负担。在本文中，我们引入了相位光场（PLF），它使用正弦条纹作为图案，并将潜在相位作为编码。借助PLF和重新格式化的相位对极平面图像（相位EPI），我们提出了一个全局成本最小化框架来估计深度。一般来说，基于EPI的深度估计会测试一组候选线，以找到强度最相似的最优线，然后将最优线的斜率转换为视差和深度。基于这一原理，对于相位EPI，我们在候选线中提出了一个具有加权相位方差的成本，并证明该成本是一个凸函数。之后，利用甲虫触角搜索（BAS）优化算法找到最优线，从而获得深度。最后，引入双边滤波器以进一步提高深度质量。仿真和实际实验结果表明，所提出的方法能够生成准确的深度图，尤其是在边界区域。此外，与具有可比深度质量的最新重聚焦方法相比，所提出的方法实现了约5.9倍的加速，因此能够促进实际应用。
