Confidence Measures of Optical Flow Estimation Suitable for Multi-Frame Super-Resolution

Multi-frame super-resolution of images containing complex motion fields remains an elusive target requiring precise estimation of such motion between the frames. Although accuracy of optical flow algorithms has been increasing steadily it is not yet sufficient to provide confident subpixel resolution enhancement during super-resolution reconstruction. One way to approach the problem is to augment optical flow estimation input into super-resolution procedure by reliable confidence measure. Numerous optical flow confidence or conversely uncertainty measures have been developed recently. However, their performance has been studied and evaluated with respect to error metric involving optical flow vectors, namely angular and end point errors averaged over the entire image. Super-resolution processing requires consideration of interpolation error i.e. the difference between the reference image and the motion compensated image. This work presents comparative evaluation of optical flow confidence measures and makes conclusions of their usefulness to applications utilizing image compensation.