School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2022 Aug 21;22(16):6291. doi: 10.3390/s22166291.
Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency-in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information-in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy.
光场（LF）图像深度估计是 LF 相关应用的关键技术，例如 3D 重建、目标检测和跟踪。LF 图像的重聚焦特性为深度估计提供了丰富的信息；然而，在遮挡区域、边缘区域、噪声干扰等情况下，仍然具有挑战性。由于 LF 的多向性和像素一致性的特点，其视差平面图像（EPI）可以有效地进行深度估计，其中 LF 深度估计被转换为计算 EPI 斜率。本文提出了一种基于方向关系模型和注意力机制的 EPI LF 深度估计算法。与子孔径 LF 深度估计方法不同，该方法以 EPIs 作为输入图像。具体来说，使用方向关系模型分别提取水平和垂直 EPIs 的方向特征。然后，使用结合通道注意力和空间注意力的多视点注意力机制，为 EPI 斜率信息赋予更多权重。随后，使用多个残差模块消除干扰 EPI 斜率信息的冗余特征，其中使用小步长卷积操作以避免丢失关键的 EPI 斜率信息。实验结果表明，与对比算法相比，所提出的算法在准确性方面表现更好。
