Fast and Robust Multiframe Super Resolution

TLDR

Super‑resolution reconstructs high‑resolution images from low‑resolution inputs, but existing methods are highly sensitive to data and noise models, limiting their practical use. The study proposes an L1‑norm minimization approach with bilateral‑prior regularization to overcome limitations of existing super‑resolution methods. The method uses L1‑norm minimization with bilateral‑prior regularization, yielding a computationally inexpensive algorithm robust to motion and blur errors and producing sharp‑edge images. Simulations show the approach outperforms other super‑resolution techniques.

Abstract

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their short-comings. We propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.