Image super-resolution via Dual-Manifold Clustering and Subspace Similarity
In this paper, we consider the problem of example based single image super-resolution. Our main contribution is introducing a new framework that makes no assumption about the structural similarity between the high-resolution (HR) and low-resolution (LR) manifolds. Instead, we use a subspace affinity measure to exploit the similarity between each HR and LR subspace. First, we train both LR and HR manifolds independently, and then, by using subspace similarity we find the closest HR subspace to each LR subspace. Each patch from the LR test image is projected onto the LR trained manifold to find the closest LR subspace. Finally, the corresponding HR subspace is selected to reconstruct the HR version of the test patch. We refer to the proposed framework Dual-Manifold Clustering and Subspace Similarity (DMCSS). The experimental results showed that DMCSS achieves clear visual improvements and an average of 1dB improvement in PSNR over state-of-the-art algorithms in this field.