Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR known as Residual Channel Attention Network (RCAN). In particular, we use RCAN to further upsample and restore the imagery to produce the final SR image estimate. We demonstrate that this system is superior to applying RCAN directly to rectangularly sampled LR imagery with equivalent sample density. The theoretical advantages of hexagonal sampling are well known. However, to the best of our knowledge, the practical benefit of hexagonal sampling in light of modern processing techniques such as RCAN SR is heretofore untested. Our SR system demonstrates a notable advantage of hexagonally sampled imagery when employing a modified RCAN for hexagonal SR.

翻译：超分辨率(SR)的目的是提高图像的分辨率。应用包括安全、医疗成像和对象识别。我们提出一个基于深深深学习的低分辨率图像样本作为输入,并生成一个矩形抽样SR图像作为输出。关于培训和测试,我们使用一个现实的观测模型,其中包括来自分解的光降解和来自探测器集成的传感器降解。我们的SR方法首先使用非统一的内插来部分采集观测到的六边形图像并将其转换成矩形网格。然后我们利用一个为斯洛伐克共和国设计的、称为残余通道关注网络(RCAN)的先进电动神经网络(CNN)结构。特别是,我们利用RCAN来进一步增殖并恢复图像以生成最后的SR图像估计值。我们证明,这个系统优于直接将RCAN用于具有同等样本密度的矩形抽样LM图像中。六边取样的理论优势是众所周知的。然而,我们所了解的最先进的SRNCAN图像样本的先进性优势是,当我们将RCAN图像用于最新的RCAN取样系统时,我们将其的显著的六边光学样品用于对RCAN的升级。