A confidence growing model for super-resolution

Sina Lin, Zengchang Qin, Renjie Liao, Tao Wan

Abstract: Single image super-resolution (SR) aims at generating a high-resolution (HR) image from one low-resolution (LR) input. In this paper, we focus on single image SR by using a confidence growing model based on an example-based super resolution approach. Compared to previous works that reconstruct high-resolution image in a raster scan order, the new proposed method reconstructs the patches using a new confidence measure. More confident reconstructions are propagated to neighboring areas by enforcing a smoothness constraint in selecting patches. We also adopt hierarchical clustering to construct a training set to speed up processing. Experimental results demonstrate that this simple method outperforms existing state-of-the-art algorithms on a the given benchmark SR test images.