Halo-Free Multi-Exposure Image Fusion Based on Sparse Representation of Gradient Features
AbstractDue to sharp changes in local brightness in high dynamic range scenes, fused images obtained by the traditional multi-exposure fusion methods usually have an unnatural appearance resulting from halo artifacts. In this paper, we propose a halo-free multi-exposure fusion method based on sparse representation of gradient features for high dynamic range imaging. First, we analyze the cause of halo artifacts. Since the range of local brightness changes in high dynamic scenes may be far wider than the dynamic range of an ordinary camera, there are some invalid, large-amplitude gradients in the multi-exposure source images, so halo artifacts are produced in the fused image. Subsequently, by analyzing the significance of the local sparse coefficient in a luminance gradient map, we construct a local gradient sparse descriptor to extract local details of source images. Then, as an activity level measurement in the fusion method, the local gradient sparse descriptor is used to extract image features and remove halo artifacts when the source images have sharp local changes in brightness. Experimental results show that the proposed method obtains state-of-the-art performance in subjective and objective evaluation, particularly in terms of effectively eliminating halo artifacts. View Full-Text
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Shao, H.; Jiang, G.; Yu, M.; Song, Y.; Jiang, H.; Peng, Z.; Chen, F. Halo-Free Multi-Exposure Image Fusion Based on Sparse Representation of Gradient Features. Appl. Sci. 2018, 8, 1543.
Shao H, Jiang G, Yu M, Song Y, Jiang H, Peng Z, Chen F. Halo-Free Multi-Exposure Image Fusion Based on Sparse Representation of Gradient Features. Applied Sciences. 2018; 8(9):1543.Chicago/Turabian Style
Shao, Hua; Jiang, Gangyi; Yu, Mei; Song, Yang; Jiang, Hao; Peng, Zongju; Chen, Feng. 2018. "Halo-Free Multi-Exposure Image Fusion Based on Sparse Representation of Gradient Features." Appl. Sci. 8, no. 9: 1543.
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