Recent years have witnessed the great advances of deep neural networks (DNNs) in light field (LF) image super-resolution (SR). However, existing DNN-based LF image SR methods are developed on a single fixed degradation (e.g., bicubic downsampling), and thus cannot be applied to super-resolve real LF images with diverse degradations. In this paper, we propose the first method to handle LF image SR with multiple degradations. In our method, a practical LF degradation model that considers blur and noise is developed to approximate the degradation process of real LF images. Then, a degradation-adaptive network (LF-DAnet) is designed to incorporate the degradation prior into the SR process. By training on LF images with multiple synthetic degradations, our method can learn to adapt to different degradations while incorporating the spatial and angular information. Extensive experiments on both synthetically degraded and real-world LFs demonstrate the effectiveness of our method. Compared with existing state-of-the-art single and LF image SR methods, our method achieves superior SR performance under a wide range of degradations, and generalizes better to real LF images. Codes and models are available at https://github.com/YingqianWang/LF-DAnet.
翻译:近些年来,在光场图像超分辨率(SR)方面,深神经网络(DNN)取得了巨大进步;然而,现有的基于DNN的LF图像SR方法是在单一固定降解(例如双立下取样)的基础上开发的,因此无法应用于具有多种降解的超溶性真实的LF图像。在本文件中,我们提出了处理具有多种降解的LF图像的LF图像的首个方法。在我们的方法中,一个考虑到真实的LF图像降解过程的模糊和噪音的实用的LF降解模型正在形成。然后,一个基于DNN的LF图像SR(LF-DAnet)的降解网络(LF-DAnet)的设计是为了将降解纳入SR进程之前的降解过程。通过对多合成降解的LF图像的培训,我们的方法可以学会适应不同的降解。关于合成退化和真实世界的LFLFLFLF的大规模实验证明了我们的方法的有效性。与现有的状态、艺术的单一和LF图像的降解方法相比,我们的方法可以达到高级的高级SR-ROFM的模型。