Finding the right exemplars for reconstructing single image super-resolution

Exemplar-based methods have shown their potential in synthesizing novel but visually plausible contents for image super-resolution (SR), by using the implicit knowledge conveyed by the exemplar database. In practice, however, it is common that unwanted artifacts and low quality results are produced due to the using of inappropriate exemplars. How are the 'right' exemplars defined and identified? This fundamental issue has not be well addressed in these methods. This paper proposes a novel solution to this issue by learning a new distance metric in the LR space, such that affinity structure of the LR space under the new metric is as close to that of the HR space. Based on this learned best metric, appropriate exemplars can be identified. In addition, the proposed method is able to automatically determine the appropriate number of exemplars to use. Extensive experiments have shown that our method is able to handle regions with different properties and to obtain visually appealing super-resolution results with sharp details and smooth edges.