The Accurate Estimation of Disparity Maps from Cross-Scale Reference-Based Light Field

This paper addresses the problem of disparity map accurate estimation in the cross-scale reference-based light field, which consists several low-quality images arranged around one central high-resolution (HR) image. In the framework, we use a HR image-guidance CNN (HRIG-CNN) for estimating the disparity map in the HR level. Specifically, we first calculate the coarse disparity map using our cross-pattern strategy, which can blend the multiple disparity maps. And then, we refine this coarse disparity map using HRIG-CNN for obtaining high-quality disparity map, which contains detail information and preserve edge information. With the HR image guidance, our HRIG-CNN achieves state-of-the-art for obtaining disparity map in such hybrid light field condition. In the end, we provide both quantitative and qualitative evaluations on different methods, and demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms.