Based on the basic assumption that low-resolution (LR) feature space and high-resolution (HR) feature space share a similar manifold geometry structure, neighbor embedding based super-resolution methods try to solve the reconstruction weights according to LR feature space, but neglect the geometry structure of HR space. In practice there are many different HR patches corresponding to a similar LR patch due to the degradation of LR images, which makes the assumption do not hold any more. This will bring noisy or blurred effect to the corresponding estimated HR image. To this end, we propose a new neighbor embedding based super resolution algorithm which takes the geometry structure of HR space into account both in neighbor selecting and reconstruction weights optimizing. In training phase, we jointly learned cell feature pairs for each LR-HR feature pair (locality preserving constraint). In testing phase, we take the distances between the input LR feature patch and its nearest cell pairs (anchored neighbor) into reconstruction weights optimizing (locality constraint). Experimental results show effectiveness of the proposed method compared with state-of-the-art methods.
['Leiyu Sun', 'Nannan Wang']
Anchored Neighbor Embedding based Image Super-Resolution with Locality Constraints