SILK: SCALE-SPACE INTEGRATED LUCAS-KANADE IMAGE REGISTRATION FOR SUPER-RESOLUTION FROM VIDEO

Registration between low-resolution images is a crucial step in super-resolution. Conventional methods tend to separate scale esti-mation from translation and rotation estimation. This is because the scale parameter is inherently related to the image resolution. In this paper, we present an area-based image registration technique that can simultaneously estimate translation, rotation, and scale parame-ters and also take into account differences in resolution between two images. We first develop a scale-space model that relates each refer-ence pixel to a single observation pixel with a scale parameter. This model is then easily generalized to include x-y shift and rotation parameters. By integrating the scale-space model into a non-linear least squares method, the method can iteratively estimate the trans-formation (x-y shift, rotation, and scale) in an accurate and efficient manner. We compare our proposed scale-space integrated Lucas-Kanade’s method (SILK) against Lucas-Kanade’s optical flow and scale-invariant feature transform (SIFT) matching and show that our method is suitable for super-resolution from very low resolution image sequences. Index Terms — Image registration, super-resolution, scale-space, non-linear least square