Efficient Super-Resolution by Finer Sub-Pixel Motion Prediction and Bilateral Filtering
Super-resolution reconstruction produces high-resolution images from a set of low-resolution images of the same scene. In the last two and a half decades, many super-resolution algorithms have been proposed. These algorithms are very sensitive to their assumed models of motion and noise, and computationally expensive for many practical applications. In this paper we adopt earlier reported fast prediction based sub-pixel motion estimation and a novel interpolation scheme based on the bilateral filter to produce a fast color super-resolution reconstruction that can accommodate arbitrary local motion patterns. The proposed algorithm exploits photometric proximity and available finer fractional motion information in the high resolution grid, to reconstruct enhanced super-resolved image frames. Experiments show a PSNR performance comparable to the state-of-the-art but at a fraction of their computational cost.