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Appl. Sci. 2018, 8(9), 1543; https://doi.org/10.3390/app8091543

Halo-Free Multi-Exposure Image Fusion Based on Sparse Representation of Gradient Features

1
Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
2
Faculty of Information and Intelligent Engineering, Ningbo City College of Vocational Technology, Ningbo 315100, China
3
National Key Lab of Software New Technology, Nanjing University, Nanjing 210093, China
*
Author to whom correspondence should be addressed.
Received: 6 July 2018 / Revised: 26 August 2018 / Accepted: 29 August 2018 / Published: 3 September 2018
(This article belongs to the Section Optics and Lasers)
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Abstract

Due 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
Keywords: multi-exposure fusion; halo artifact; sparse representation; gradient feature; high dynamic range imaging multi-exposure fusion; halo artifact; sparse representation; gradient feature; high dynamic range imaging
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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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.

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