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.

翻译：本文测试了深度学习方法在虹膜识别问题中提供低分辨率至高分辨率图像端到端映射的能力。我们提出了两种深度学习单图像超分辨率方法：堆叠自编码器与卷积神经网络，采用尽可能轻量化的结构以实现快速处理、保留局部信息并减少伪影。我们利用包含1,872张近红外虹膜图像的数据库进行质量评估与识别实验验证了这些方法，结果证明了深度学习方法相较于对比算法的优越性。