Multi-Scale Exemplary Based Image Super-Resolution with Graph Generalization

Exemplary based image super-resolution (SR) approaches decompose low-resolution (LR) images into multiple overlapped local image patches, and find the best high-resolution (HR) pair for each LR patch to generate processed HR images. The super-resolving process models these multiple HR/LR patches in a Markov Network where there exists both confidence constraint between the LR patch and the selected HR patch from database, and the harmonic constraint between neighboring HR patches. Such a graphical structure, however, makes the optimization process extremely slow, and therefore extensive research efforts on improving the efficiency of exemplary based SR methods have been reported. In this chapter, the focus is on those methods that aim at generating high quality HR patches from the database, while ignoring the harmonic constraint to speed up processing, such as those that model the problem as an embedding process, or as a feature selection process. As shown in this chapter, these approaches can all be regarded as a coding system. The contributions of the paper are two-fold: First, the chapter introduces a coding system with resolution-invariance property, such that it is able to handle continues-scale image resizing as compared to traditional methods that only support single integer-scale upsizing; second, the author generalizes the graphical model where the typical non-linear coding process is approximated by an easier-to-compute function. In this way, the SR process can be highly parallelized by modern computer hardware. As demonstrated by the chapter, the proposed system gives very promising image SR results in various aspects.