Fast Super-Resolution Algorithm Based on Dictionary Size Reduction Usingk-Means Clustering

Abstract

This paper proposes a computationally efficient learning‐based super‐resolution algorithm using k‐means clustering. Conventional learning‐based super‐resolution requires a huge 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. Experimental results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.

Image Quality Assessment by Upsampling Methods Based on Spatial Spectrum Extrapolation

“…Secondly, the clustered dictionary is used to extract some salient patches that will form the output set. The clustering step is partially similar to the method proposed in [77], where, however, the k-means clustering is done by referring only to the LR patches and is not a joint procedure.…”

Approach to Super-Resolution Through the Concept of Multicamera Imaging

“…• The concept of learning-based SR [28,29] has not been developed for MC applications. It exploits the prior knowledge between the HR examples and the corresponding LR examples through the so-called learning process.…”