Article ID Journal Published Year Pages File Type
4968745 Computer Vision and Image Understanding 2016 12 Pages PDF
Abstract
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.
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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