关键词: 光场, 深度信息估计, 遮挡干扰, 图像聚类, 目标函数, MAP-MRF框架

Abstract:

To solve the low accuracy problem caused by occlusion interference of light field depth estimation, the light field depth information estimation algorithm for multi-occlusion interference handling is proposed. The occluder analysis of the angular patch image is carried out to analyze the position distribution characteristics of the occluder. Based on the classification idea, an improved Affinity Propagation（AP） clustering algorithm is proposed to classify the angular patch image, to separate the occluder from the scene point. The cost function combining the pixel intensity information entropy and the central variance is constructed on the angular patch image after separating the occluder. By minimizing the cost function, the initial depth estimation is obtained. The smooth constrained energy function based on MAP-MRF（Maximum A Posteriori Estimated Markov Random Field） framework is constructed and solved by the Graph Cut Algorithm（CCA） to obtain the final depth estimation. The experimental results show that compared with the existing depth estimation algorithm, the proposed algorithm improves the estimation accuracy at the occlusion.

Key words: light field, depth estimation, occlusion interference, image clustering, cost function, MAP-MRF framework