A New Super-Resolution Algorithm Based on Areas Pixels and the Sampling Theorem of Papoulis

摘要

In several application areas such as art, medicine, broadcasting and e-commerce, high-resolution images are needed. Super-resolution is the algorithmic process of increasing the resolution of an image given a set of displaced low-resolution, noisy and degraded images. In this paper, we present a new super-resolution algorithm based on the generalized sampling theorem of Papoulis and wavelet decomposition. Our algorithm uses an area-pixel model rather than a point-pixel model. The sampling theorem is used for merging a set of low-resolution images into a high-resolution image, and the wavelet decomposition is used for enhancing the image through efficient noise removing and high-frequency enhancement. The proposed algorithm is non-iterative and not time-consuming. We have tested our algorithm on multiple images and used the peak-to-noise ratio, the structural similarity index and the relative error as quality measures. The results show that our algorithm gives images of good quality.