Multiview high dynamic range reconstruction via gain estimation Multiview high dynamic range reconstruction is a challenging problem, especially if the multiview 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 backlighting 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 multiview 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 stateoftheart methods in both objective and subject comparisons. These results suggest that our method is suitable to improve the visual quality of multiview low dynamic range images captured in low backlighting conditions via commercial cameras sparsely located among each other.

DualStreams Global Guided Learning for High Dynamic Range Image Reconstruction High dynamic range (HDR) images capture the luminance information of the real world and have more detailed information than low dynamic range (LDR) images. In this paper, we propose a dualstreams global guided endtoend learning method to reconstruct HDR image from a single LDR input that combines both global information and local image features. In our framework, global features and local features are separately learned in dualstreams branches. In the reconstructed phase, we use a fusion layer to fuse them so that the global features can guide the local features to better reconstruct the HDR image. Furthermore, we design mixed loss function including multiscale pixelwise loss, color similarity loss and gradient loss to jointly train our network. Comparative experiments are carried out with other stateoftheart methods and our method achieves superior performance.