Novel Super-Resolution Method Based on High Order Nonlocal-Means

TL;DR

This paper introduces a high-order nonlocal-means super-resolution technique that leverages kernel regression to improve image reconstruction without explicit motion estimation, demonstrating competitive performance.

Contribution

It generalizes the Non-Local Means method to higher orders using kernel regression for super-resolution, a novel approach in image reconstruction.

Findings

Comparable or improved super-resolution results compared to existing methods

Effective without explicit sub-pixel motion estimation

Generalizes NLM to higher orders for better reconstruction

Abstract

Super-resolution without explicit sub-pixel motion estimation is a very active subject of image reconstruction containing general motion. The Non-Local Means (NLM) method is a simple image reconstruction method without explicit motion estimation. In this paper we generalize NLM method to higher orders using kernel regression can apply to super-resolution reconstruction. The performance of the generalized method is compared with other methods.