Depth estimation method based on adaptive occlusion handling for light-field imaging systems

To solve the problem of poor depth estimation due to the influence of occlusion in light-field imaging systems, an embeddable adaptive occlusion-aware module (AOAM) is proposed to effectively compensate for the deficiencies of most existing frameworks. Considering the low computational resource consumption, an adaptive occlusion optimization mode is built that introduces a voting strategy. The beam propagation characteristics are analyzed to filter the disparity values, and the adaptive voting cost is utilized to achieve regional partitioning and noise reduction in the global domain. The superiority of the proposed method is validated on a common light-field dataset.