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

• Main Authors: Yu-Hsiu Lin, Kai-Lung Hua, Hsin-Han Lu, Wei-Lun Sun, Yung-Yao Chen
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Sensors
• Subjects: fuzzy logic; intelligent vision sensing; exposure fusion; coarse-to-fine tuning; detail manipulation
• Online Access: https://www.mdpi.com/1424-8220/19/21/4743

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.