An Adaptive Exposure Fusion Method Using fuzzy Logic and Multivariate Normal Conditional Random Fields.

Clicks: 311
ID: 94334
2019
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Abstract
High dynamic range (HDR) has wide applications involving intelligent vision sensing which includes enhanced electronic imaging, smart surveillance, self-driving cars, intelligent medical diagnosis, etc. Exposure fusion is an essential HDR technique which fuses different exposures of the same scene into an HDR-like image. However, determining the appropriate fusion weights is difficult because each differently exposed image only contains a subset of the scene's details. When blending, the problem of local color inconsistency is more challenging; thus, it often requires manual tuning to avoid image artifacts. To address this problem, we present an adaptive coarse-to-fine searching approach to find the optimal fusion weights. In the coarse-tuning stage, fuzzy logic is used to efficiently decide the initial weights. In the fine-tuning stage, the multivariate normal conditional random field model is used to adjust the fuzzy-based initial weights which allows us to consider both intra- and inter-image information in the data. Moreover, a multiscale enhanced fusion scheme is proposed to blend input images when maintaining the details in each scale-level. The proposed fuzzy-based MNCRF (Multivariate Normal Conditional Random Fields) fusion method provided a smoother blending result and a more natural look. Meanwhile, the details in the highlighted and dark regions were preserved simultaneously. The experimental results demonstrated that our work outperformed the state-of-the-art methods not only in several objective quality measures but also in a user study analysis.
Reference Key
lin2019ansensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lin, Yu-Hsiu;Hua, Kai-Lung;Lu, Hsin-Han;Sun, Wei-Lun;Chen, Yung-Yao;
Journal sensors
Year 2019
DOI
E4743
URL
Keywords
Fuzzy logic coarse-to-fine tuning detail manipulation exposure fusion intelligent vision sensing

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