High accuracy sub-pixel image registration under noisy condition

Image registration plays an important role in many image processing applications. A key problem is that the accuracy of registration can be severely affected by noise. This paper presents a sub-pixel registration method for noisy images. In particular, we investigate how to estimate transitional shift to a high precision using the noisy phase data in frequency domain. Based on theoretical analysis, we find that the noise-caused phase change for every frequency component of high signal-to-noise ratio (SNR) can be well approximated by a Gaussian distribution.

Furthermore, we show that the reliability of phase data can also be measured by the SNR of corresponding frequency component. A noise-robust registration framework is proposed to utilize high-SNR frequency components adaptively, while masking out the components of SNR lower than a threshold. Experiments demonstrate that the proposed method is superior to existing image registration methods in the presence of noise.