Locality preserving constraints for super-resolution with neighbor embedding
In this paper, we revisit the manifold assumption which has been widely adopted in the learning-based image super-resolution. The assumption states that point-pairs from the high-resolution manifold share the local geometry with the corresponding low-resolution manifold. However, the assumption does not hold always, since the one-to-multiple mapping from LR to HR makes neighbor reconstruction ambiguous and results in blurring and artifacts. To minimize the ambiguous, we utilize Locality Preserving Constraints (LPC) to avoid confusions through emphasizing the consistency of localities on both manifolds explicitly. The LPC are combined with a MAP framework, and realized by building a set of cell-pairs on the coupled manifolds. Finally, we propose an energy minimization algorithm for the MAP with LPC which can reconstruct high quality images compared with previous methods. Experimental results show the effectiveness of our method.