A mutual local-ternary-pattern based method for aligning differently exposed images

• Published in: Computer vision and image understanding Vol. 152; pp. 67 - 78
• Main Authors: Wu, Shiqian, Yang, Lingxian, Xu, Wangming, Zheng, Jinghong, Li, Zhengguo, Fang, Zhijun
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2016
• Subjects: Coarse-to-fine technique; HDR imaging; Histogram-based matching; Image mapping function; Image registration; Local ternary pattern; Multi-exposed images; Image registration; Local ternary pattern; Multi-exposed images; HDR imaging; Histogram-based matching; Image mapping function; Coarse-to-fine technique
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2016.07.010

•Order feature is the invariant representation of multi-exposed images. However, saturation yield inconsistent order features of multi-exposed images.•A novel mutual local ternary pattern (MLTP) is proposed to cope with saturation and large-variation intensities.•Image rotation is initially detected by the histogram-based matching.•A linear model is derived for fast image registration and coarse-to-fine technique is implemented to cope with large movement. Saturation and large intensity variations occurred in multi-exposed images offer great challenges to align these images. In this paper, a mutual local-ternary-pattern (MLTP) is proposed to represent differently exposed images for image registration. Different from the classical local ternary pattern (LTP) and its variants, the proposed MLTP has two salient properties: (1) The ternary pattern of one image is not only determined by itself, but also relied on its counterpart; (2) The MLTP is grayscale-adaptive. It is analyzed that the proposed MLTP is a good representation to preserve consistency of differently exposed images. Based on the MLTP-coded images, an efficient linear model derived from Taylor expansion is presented to estimate motion parameters. To improve accuracy and efficiency, image rotation is initially detected by the histogram-based matching, and coarse-to-fine technique is implemented to cope with possibly large movement. Extensive experiments carried out on a variety of synthesized and real multi-exposed images demonstrate that the proposed method is robust to 10 exposure values (EV), which is superior to other methods and current commercial HDR tools.