![]() ![]() LF cameras collect and record light from different directions in the scene, which can simultaneously record spatial and angular information of light rays incident at pixels of the tensor by inserting a microlens array between the main lens and image sensor. L( x, y, u, v) can be viewed as an assignment of an intensity value to the ray passing through ( x, y) and ( u, v). It can be represented by the two-plane parametrization as L( x, y, u, v), where the ( x, y) plane contains the focal points of the views, and the ( u, v) plane means image plane. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy.Ī light field (LF) is defined as the flow of light in every 3D space. 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. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. 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. 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. Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. ![]()
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