Enhanced Bidirectional Motion Estimation Using Feature Refinement for HDR Imaging

Abstract

We propose a high dynamic range (HDR) image synthesis algorithm based on enhanced bidirectional motion estimation using feature refinement. First, we extract multiscale features from input low dynamic range (LDR) images and then estimate accurate motion vector fields between them in a coarse-to-fine manner via progressive refinement. Then, we estimate adaptive local kernels to merge only valid information in the spatio-exposed neighboring pixels for synthesis. Finally, we refine the initially merged image by exploiting global information to further improve synthesis performance. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms in quantitative and qualitative comparisons.