An efficient method of estimating edge locations with subpixel accuracy in noisy images

An efficient algorithm for estimating real edge locations to subpixel values in digital imagery is described. To estimate the real boundary between two adjacent pixels, we first define a 1D edge operator based on the moment invariant. To extend it to 2D data, the edge orientation of each pixel is estimated using the LSE (least squares error) method which fits a set of pixels around the edge boundary to a line/circle equation. Then, using the pixels along the line perpendicular to the estimated edge orientation, the real boundary is estimated with subpixel accuracy. Experimental results tested on real images have shown that the proposed method is robust to local noise, while maintaining a low measurement error.