The importance of edge fidelity in image resizing is well established. Isophotes, or connected pixels of equal intensity, are essential to human perception of static images and interpolation methods that disrupt isophote curvature produce distracting artifacts. We introduce a new image resizing algorithm based on the principles of optical flow. The optical flow equation assumes that for every pixel in a given video frame, there exists an isointense pixel in adjacent frames. For video, this assumption implies that subsequent frames are reconfigurations of the same pixels. Here, we apply the optical flow equation to adjacent rows and columns of single images. The physical basis for optical flow in video (objects are moving) does not apply to static images. However, the the use of the optical flow equation amounts to asserting that each pixel is a member of an isophote with curvature that can be approximated locally with a straight line. Our implementation is fully separable and outperforms both traditional and recently proposed interpolators including NEDI and iNEDI.