A Mutual Local-Ternary-Pattern Based Method For Aligning Differently Exposed Images

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. (C) 2016 Elsevier Inc. All rights reserved.