Quantitative Evaluation of Image Interpolation Methods Supposed to Be Applied to Sub-Pixel Estimation

Abstract

Sub-pixel estimation of the corresponding positions in area-based matching is often conducted to obtain the precise locations of objects using stereo images. Such sub-pixel estimation methods as least squares matching and cross correlation method using sub-pixel shifted images require appropriate interpolation of the gray values of the window corresponding to the template. Since there are few reports on comparison of image interpolation methods in sub-pixel estimation accuracy, we decided to investigate performance of interpolation methods applied to sub-pixel estimation. This paper reports the preparatory experiment conducted in order to evaluate image interpolation methods quantitatively by using 54 diverse images. Three popular methods in remote sensing and digital photogrammetry: bi-linear interpolation (BL), bi-cubic interpolation (BC), and cubic convolution (CC) were investigated. The experiment results demonstrate that the interpolation accuracy of all three methods is correlated to texture measures of the image such as grey level difference vector measures and spatial frequency. Furthermore, the experiment results show that BC and CC can produce better interpolation results than BL, when an image has no noise or smaller noises. On the other hand, BL and BC can produce better interpolation results than CC, when an image has larger noises.