A robust method for aligning large-photometric-variation and noisy images

We propose a novel robust method to accurately align large-photometric-variation and noisy images emerged from high dynamic range (HDR) imaging. First, the keypoints are detected from optimal multi-binary images to eliminate the noise and photometric effects. The feature descriptor, which is translation-, rotation- and scale-invariant and robust to noise and photometric changes, is then developed, and feature matching is implemented efficiently by use of the structure information of the keypoints. Finally, mutual information (MI) is employed in the RANSAC method for homography estimation and performance assessment, which makes the results accurate and stable. Experiments carried out on synthesized images demonstrate that the proposed method is much more robust to both photometric changes and noise than the SIFT method, the state-of-the-art alignment method.