Fast Example-Based Super-Resolution Using Manifold Learning

Abstract

This paper presents a new method for single-frame Super-Resolution (SR), by combining Example-based SR and neighbor embedding based SR (NE-based SR). Example-based SR attempts to generate High-Resolution (HR) image through estimating the High-Frequency (HF) components that are lost in the input Low-Resolution (LR) image. This method usually can achieve acceptable HR images if enough amounts of similar training samples are prepared. However, the HF component is approximated by only one training sample, which easily produces noise and artifacts. On the other hand, NE-based SR recovers HR image using manifold learning - Locally Linear Embedding, which represents any LR input and its corresponding HR one by a weighted linear combination of several training patches. The NE-based SR need to prepare large-scale training database with both intensity and structure variation, which will lead to high computation. This study combines these two methods to only estimate the HF components using several training samples. Moreover, we extend the proposed method to a fast version by processing only the patches with large variance. Experimental results show that the reconstructed HR images by our proposed approach are much better than those by conventional methods and interpolation techniques, and at the same time the computation is much faster.