# Image interpolation using Shearlet based iterative refinement

arXiv: https://arxiv.org/abs/1308.1126 · HTML: https://mlsanity.com/paper/1308.1126/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2013.

## Built on by (most cited first, 2 of 2)

- [Decision boundaries and convex hulls in the feature space that deep learning functions learn from images](https://mlsanity.com/paper/2202.04052/) (2022)
- [Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods](https://mlsanity.com/paper/2112.12021/) (2021)

## Abstract (arXiv)

This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective as well as subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images.