# Exploring Deep Learning Image Super-Resolution for Iris Recognition

arXiv: https://arxiv.org/abs/2311.01241 · HTML: https://mlsanity.com/paper/2311.01241/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Iris Recognition Based on SIFT Features](https://mlsanity.com/paper/2111.00176/) (2021)

## Built on by (most cited first, 3 of 3)

- [Generative Iris Prior Embedded Transformer for Iris Restoration](https://mlsanity.com/paper/2407.00261/) (2024)
- [Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer](https://mlsanity.com/paper/2503.04707/) (2025)
- [Adaptive Deep Iris Feature Extractor at Arbitrary Resolutions](https://mlsanity.com/paper/2407.08341/) (2024)

## Abstract (arXiv)

In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local information and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms.