Training-based super-resolution algorithm using k-means clustering and detail enhancement

This paper presents a computationally efficient learning-based super-resolution algorithm using k-means clustering and detail enhancement. Conventional learning-based super-resolution requires a huge size of dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Simulation results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.