Locally adaptive regularized super-resolution on video with arbitrary motion

Regularization based super-resolution (SR) methods have been widely used to improve video resolution in recent years. These methods, however, only minimize the sum of difference between acquired low resolution (LR) images and observation model without considering video local structure. In this paper, we proposed an idea, which employs adaptive kernel regression on regularization based SR methods, to improve super-resolution performance. Arbitrary motions in input video are also considered and well modeled in our work. It is shown that the proposed idea can provide better visual quality as well as higher Peak Signal-to-Noise Ratio (PSNR) than approaches using regularized scheme or adaptive kernel regression alone.