Super-resolution from noisy image sequences exploiting a 2D parametric motion model
We propose a low cost scheme for reconstructing high resolution images from noisy, and eventually blurred image sequences. The super-resolution is achieved by an iterative back projection method. To account for noise in image sequence, we first apply a spatio-temporal Wiener filter computed via a 3D DFT. In the filtering process, we need to compensate for apparent motion to ensure proper results. Furthermore, the knowledge of subpixel motion is necessary for super-resolution. In both cases, we exploit a parametric motion model to keep a good trade-off between accuracy and computation time.