Two-direction self-learning super-resolution propagation based on neighbor embedding

Abstract

Neighbor embedding (NE) is a widely used super-resolution (SR) algorithm, but the one-to-many problem always degrades the performance of NE. The simplest way to avoid this performance degradation is to extract image features from low-resolution (LR) patches, that correctly reflect the features in the corresponding high-resolution (HR) patches. In this paper, we propose several feature extraction methods to extract patch features in LR space, and use coarse-to-fine patch matching methods to select matching patches for each test patch and find the best matching patch candidate to update the matching patch. Since finding matching patches using a training set is exhaustive work in NE, we explore the traditional local/non-local similarity prior and propose vertical similarity in the image pyramid to accelerate the matching patch search process. To accomplish this, we propose a random oscillation+horizontal propagation+vertical propagation strategy to update the matching patches. The experimental results show that the proposed method is superior to many existing self-learning-based methods, but it is inferior to many external-learning-based methods. These results show that the proposed feature extraction method and oscillation propagation method are useful for finding proper matching patches in NE.