Comparison of Dense Matching Algorithms in Noisy Image

摘要

In this paper, we compare two correlation techniques for dense matching used in image corresponding problem, namely, the Sum of Squared Difference (SSD) and Normalized Cross Correlation (NCC). Both algorithms look for part of the image that matches a template based on intensity information. The window of the template is of Voronoi size, according to each Voronoi cells. The corresponding seed relations in each cell until all pixels within each cell are processed using SSD and NCC algorithms. In our experiments compare the performance of SSD and NCC in image with additive Gaussian noise, salt & pepper noise, and speckle noise. We found that SSD is more robust to noise than NCC in all cases.