Multi-view high dynamic range reconstruction via gain estimation
Firas Abedi, Qiong Liu, You Yang · 2019
Multi-view high dynamic range reconstruction is a challenging problem, especially if the multi-view low dynamic range images are obtained from cameras arranged sparsely with limited shared view of vision among them. In this paper, we address the above challenge in addition to the back-lighting problem. We first enclose the geometry characteristic of the scene to rectify the outlier feature points. Consequently, an exposure gain is calculated according to those rectified features. After that, we extend the dynamic range for the multi-view low dynamic range images based on the estimated gain, then, generate a final high dynamic range image per view. Experimental results demonstrate superior performance for the proposed method over state-of-the-art methods in both objective and subject comparisons. These results suggest that our method is suitable to improve the visual quality of multi-view low dynamic range images captured in low back-lighting conditions via commercial cameras sparsely located among each other.