An Efficient Super-Resolution Single Image Network using Sharpness Loss Metrics for Iris

Abstract

Most of the state of the art super-resolution methods use deep networks with large filter sizes. Therefore, they need to train and store a correspondingly large number of parameters, thereby making their use difficult for mobile devices applications such as recognition of individuals from selfie images. To achieve an efficient super-resolution method, we propose an Efficient Single Image Super-Resolution (ESISR) algorithm, which takes into account a trade-off among the efficiency of the deep neural network, the size of the filters, and the sharpness of the images. To that end, the method implements a novel loss function based on the Sharpness metric. This metric turns out to be more suitable for recovering the quality of the eye images. Our method drastically reduces the number of parameters when compared with Deep CNNs with Skip Connection and Network (DCSCN): from 1,754,942 to 27,209 parameters when the image size is increased by a factor of 2 (x2), from 2,170,142 to 28,654 parameters when increased by 3 (x3), and from 2,087,102 to 64,201 parameters when increased by 4 (x4). Furthermore, the proposed method maintains the sharpness quality of the images.