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 estimation 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 parameters and also take into account differences in resolution between two images. We first develop a scale-space model that relates each reference 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 transformation (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.