# Image scaling by de la Vall'ee-Poussin filtered interpolation

arXiv: https://arxiv.org/abs/2109.13897 · HTML: https://mlsanity.com/paper/2109.13897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 83 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018)
- [Super-Resolution via Deep Learning](https://mlsanity.com/paper/1706.09077/) (2017)
- [Oversampling errors in multimodal medical imaging are due to the Gibbs effect](https://mlsanity.com/paper/2103.05964/) (2021)
- [Evaluation of quality measures for color quantization](https://mlsanity.com/paper/2011.12652/) (2020)

## Built on by (most cited first, 1 of 1)

- [Implementation of neural network operators with applications to remote sensing data](https://mlsanity.com/paper/2412.00375/) (2024)

## Abstract (arXiv)

We present a new image scaling method both for downscaling and upscaling, running with any scale factor or desired size. The resized image is achieved by sampling a bivariate polynomial which globally interpolates the data at the new scale. The method's particularities lay in both the sampling model and the interpolation polynomial we use. Rather than classical uniform grids, we consider an unusual sampling system based on Chebyshev zeros of the first kind. Such optimal distribution of nodes permits to consider near--best interpolation polynomials defined by a filter of de la Vall'ee Poussin type. The action ray of this filter provides an additional parameter that can be suitably regulated to improve the approximation. The method has been tested on a significant number of different image datasets. The results are evaluated in qualitative and quantitative terms and compared with other available competitive methods. The perceived quality of the resulting scaled images is such that important details are preserved, and the appearance of artifacts is low. Competitive quality measurement values, good visual quality, limited computational effort, and moderate memory demand make the method suitable for real-world applications.