This paper proposes a fast super-resolution (SR) algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, we cluster a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs into a specific number of groups using a specific classifier, and we compute the optimal 2D FIR filter to synthesize a high-quality HR patch from an LR patch per cluster, and store the patch-adaptive 2D FIR filters in a dictionary. In the inference stage, from the dictionary, we find the best matched candidate to each input LR patch in terms of the same classifier as the learning stage, and synthesize the HR patch by using the optimal 2D FIR filter corresponding to the best matched candidate. The experimental results show that the proposed algorithm produces HR images of similar quality to the existing SR methods on a per patch basis, while providing fast running time.
['Dong-yoon Choi', 'Byung Cheol Song']
Fast super-resolution algorithm using ELBP classifier