期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 20, 期 12, 页码 3634-3646出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2011.2150235
关键词
Image enhancement; image fusion; multi-exposure fusion; random walks
资金
- Killam Trusts
- iCORE
- Alberta Advanced Education and Technology
A single captured image of a real-world scene is usually insufficient to reveal all the details due to under-or over-exposed regions. To solve this problem, images of the same scene can be first captured under different exposure settings and then combined into a single image using image fusion techniques. In this paper, we propose a novel probabilistic model-based fusion technique for multi-exposure images. Unlike previous multi-exposure fusion methods, our method aims to achieve an optimal balance between two quality measures, i.e., local contrast and color consistency, while combining the scene details revealed under different exposures. A generalized random walks framework is proposed to calculate a globally optimal solution subject to the two quality measures by formulating the fusion problem as probability estimation. Experiments demonstrate that our algorithm generates high-quality images at low computational cost. Comparisons with a number of other techniques show that our method generates better results in most cases.
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