Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting

TL;DR

This paper introduces a key point agnostic frequency-selective mesh-to-grid image resampling method that reduces computational complexity and improves quality by using spectral weighting, suitable for various image processing applications.

Contribution

It proposes a novel resampling technique that eliminates the need for pre-estimated key points, significantly reducing runtime and enhancing resampling quality.

Findings

Up to 1.2 dB PSNR improvement over original methods.

Approximately 14.5 times faster processing.

Effective suppression of ringing artifacts.

Abstract

Many applications in image processing require resampling of arbitrarily located samples onto regular grid positions. This is important in frame-rate up-conversion, super-resolution, and image warping among others. A state-of-the-art high quality model-based resampling technique is frequency-selective mesh-to-grid resampling which requires pre-estimation of key points. In this paper, we propose a new key point agnostic frequency-selective mesh-to-grid resampling that does not depend on pre-estimated key points. Hence, the number of data points that are included is reduced drastically and the run time decreases significantly. To compensate for the key points, a spectral weighting function is introduced that models the optical transfer function in order to favor low frequencies more than high ones. Thereby, resampling artefacts like ringing are supressed reliably and the resampling quality increases. On average, the new AFSMR is conceptually simpler and gains up to 1.2 dB in terms of PSNR compared to the original mesh-to-grid resampling while being approximately 14.5 times faster.