Fast super-resolution algorithm using rotation-invariant ELBP classifier and hierarchical pattern matching

This paper proposes a fast super-resolution (SR) algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters based on a rotation-invariant classifier. 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 the rotation-invariant classifier, and choose a specific number of dominant clusters. Then, we compute the optimal 2D FIR filter(s) to synthesize a high-quality HR patch from an LR patch per cluster, and finally store the patch-adaptive 2D FIR filters in a dictionary. Also, we present a smart hierarchical addressing method for effective dictionary exploration in the inference stage. In the inference stage, the ELBP of each input LR patch is extracted in the same way as the learning stage, and the best matched FIR filter(s) to the input LR patch is found from the dictionary by the hierarchical addressing. Finally, we synthesize the HR patch by using the optimal 2D FIR filter. The experimental results show that the proposed algorithm produces better HR images than the existing SR methods, while providing fast running time.