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

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. (C) 2021 Elsevier B.V. All rights reserved.