Toward Real-World Light Field Depth Estimation: A Noise-Aware Paradigm Using Multi-Stereo Disparity Integration

Producing depth maps of a scene is essential in many light field applications. Numerous algorithms have been developed to obtain an accurate depth map on public datasets. However, real-world light field images obtained by cameras have noises produced by hardware limitations. Existing methods do not perform well on noisy scenes. In this paper, we propose a noise-aware light field depth estimation algorithm which is insensitive to noise. First, an optical flow approach is used to compute disparities between the central view and each of the remaining views, then a weighted integration method is used to reduce calculation errors. Second, a refinement framework, which is measured using the refocus image and angular patch, is proposed to handle occlusion and texture-less parts. Third, a K-means clustering-based strategy is used to improve noise capability. Finally, a depth map is regularized by a Markov random field (MRF). Extensive experiments are conducted on synthetic datasets and real-world datasets. Both quantitative and qualitative results have demonstrated the superiority and robustness of our method.