# Single image super-resolution by approximated Heaviside functions

arXiv: https://arxiv.org/abs/1503.03630 · HTML: https://mlsanity.com/paper/1503.03630/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2015.

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

- [A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries](https://mlsanity.com/paper/2309.10935/) (2023)

## Abstract (arXiv)

Image super-resolution is a process to enhance image resolution. It is widely used in medical imaging, satellite imaging, target recognition, etc. In this paper, we conduct continuous modeling and assume that the unknown image intensity function is defined on a continuous domain and belongs to a space with a redundant basis. We propose a new iterative model for single image super-resolution based on an observation: an image is consisted of smooth components and non-smooth components, and we use two classes of approximated Heaviside functions (AHFs) to represent them respectively. Due to sparsity of the non-smooth components, a L1 model is employed. In addition, we apply the proposed iterative model to image patches to reduce computation and storage. Comparisons with some existing competitive methods show the effectiveness of the proposed method.