## 2 Related Work  
Based on different representations of light field data, light field depth estimation algorithms can be divided into two categories: depth estimation algorithms based on interpreted data and depth estimation algorithms based on original light field images. Depth estimation algorithms based on interpreted data preprocess the multi-angle information captured by light field cameras, converting them into different forms of interpretive data such as focus stacks, epipolar plane images, or sub-aperture images [14; 15; 16; 17; 18], and then extract depth information from these data. Such algorithms cannot fully utilize the geometric information contained in original light field images and have poor depth value accuracy in weak texture regions. Depth estimation algorithms based on original light field images can directly extract depth information from original images, avoiding information loss and error accumulation that may be caused by data preprocessing. Therefore, when facing various complex scenes, depth estimation algorithms based on original light field images can accurately extract depth information. In recent years, research on light field depth estimation based on original images has made certain progress. Some researchers believe that the neighborhood selection of microimages significantly affects estimation accuracy. [PERSON] and [PERSON] [19] developed an adaptive strategy to select only certain candidate microimages to consider different defocus blur amounts between different types of microimages. [PERSON] and [PERSON] [20] further explored the impact of different lens selection strategies on depth estimation accuracy. Meanwhile, some researchers have also found that feature point matching and disparity estimation between microimages significantly affect estimation accuracy. For example, [PERSON] [21] and [PERSON] et al. [22] studied using feature points matched between microimages for triangulation to estimate virtual depth, but the algorithm could extract too few feature points, leading to limited estimable effective depth values. [PERSON] [23] achieved metric depth estimation for each microimage by minimizing dense reprojection errors in adjacent microimages under depth assumptions. [PERSON] et al. [24]. proposed a switching mechanism between the intensity domain and the local phase quantization (LPQ)domain to solve the problem of different blur amounts during matching. [PERSON] et al. (2019) introduced probabilistic depth estimation by obtaining virtual depth hypotheses through epipolar correspondences in adjacent microimages and fusing multiple hypotheses and their variances in a manner similar to Kalman filtering to improve depth value estimation accuracy. [PERSON] (2019) significantly improved the disparity estimation accuracy of multi-focus camera images by introducing defocus information and blur features. However, due to the internal structure of light field cameras and the specificity of light field data, depth estimation algorithms based on original light field images still suffer from poor accuracy ([PERSON] et al., 2019; [PERSON] et al., 2020), specifically manifested as: (1) Existing neighborhood selection methods are still limited by fixed neighborhood selection ranges, failing to dynamically select neighborhood microimages using visual features, resulting in poor matching results. (2) The microlens array disrupts the prominent features of objects, reducing microimage texture richness and easily causing mismatching problems, which leads to decreased depth estimation accuracy. To address these limitations, Our algorithm introduces a multi-constraint approach that integrates photometric consistency, blur features, and geometric consistency to adaptively select optimal neighborhood micro-image sets for depth estimation. Additionally, we incorporate plane priors into our matching algorithm to facilitate the construction of multi-view aggregation matching costs and optimize the matching costs for pixels located in texture-weak regions.