Image super-resolution based on locality-constrained linear coding

This paper presents a learning-based method called image super-resolution (SR) for generating a high-resolution (HR) image from a single low-resolution (LR) image. Recent research investigated the image SR problem using sparse coding, which is based on good reconstruction of any image local patch by a sparse linear combination of atoms from an overcomplete dictionary. However, sparse-coding-based SR (ScSR) generally takes a significant amount of computational time to compute an HR image. Further, it can yield only a global dictionary D = [D_h;D_l] by jointly training the concatenated HR and LR image local patches, which results in no accurate correspondence between the HR and LR dictionaries. Therefore, we propose the generation of an HR image using a linear combination of several anchor points (codes) for a local patch based on locality-constrained linear coding (LLC), which is a fast implementation of local coordinate coding (LCC). In the proposed LLC-based strategy, each local patch is represented by a weighted linear combination of its nearer codes in a predefined codebook, and the linear weights become its local coordinate coding. Experimental results show that the recovered HR images with our proposed approach can achieve comparable performance at a processing time much shorter than those of conventional methods.