Advances on image interpolation based on ant colony algorithm

TL;DR

This paper introduces an improved image interpolation method using a global weighting ant colony algorithm, enhancing high-resolution image scaling by leveraging pheromone matrix information for better performance.

Contribution

The paper proposes a novel global weighting ant colony algorithm for image interpolation, differing from previous local weighting schemes, leading to improved image scaling quality.

Findings

Higher performance demonstrated through experiments

Global weighting improves interpolation accuracy

Outperforms previous algorithms in quality metrics

Abstract

This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high-resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses a local weighting scheme. The strength of the proposed global weighting of the AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper.