Light field image depth estimation is a critical technique in applications such as light field 3D reconstruction, target detection and tracking. The refocusing property of light field image provided rich information for depth estimation, however it was still challenging in the case of occlusion region, edge region, noise interference, etc. Therefore, a depth estimation algorithm based on the consistency of epipolar plane image (EPI) slant pixels and the difference of epipolar plane image regions was proposed to solve the occlusion and noise problems. The consistency of EPI slant pixels was adopted by the spinning linear operator (SLO) color entropy metric, which could improve the accuracy of depth map edges as well as the noise immunity; the difference of EPI regions was measured by the chi-square χ² metric of the spinning parallelogram operator (SPO), which could improve the accuracy of the depth gradient area of the depth map,and the two metrics were fused with confidence weighting, which could reduce the interference of occluded regions and noise. In addition, the color similarity of the pixel neighborhood was fully utilized, and post-processing was performed with a guided edge-preserving filter and Markov random field (MRF) global optimization strategy to further reduce the edge error of the depth map to obtain an accurate depth map with occluded edges. Experiments were conducted on the HCI light field data set and compared with the classic light field depth estimation algorithm. The results show that the algorithm has a significant improvement in both subjective quality and objective indicators.;that the proposed method outperformed state-of-the-art depth estimation methods. Compared with the classical light field depth estimation algorithm, proposed method had significant improvement on both subjective and objective qualities.