论文标题
用于单图像超分辨率的深迭代剩余卷积网络
Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
论文作者
论文摘要
由于其功能强大的特征表示功能，深度卷积神经网络（CNN）最近在单像超级分辨率（SISR）任务方面取得了巨大成功。最新的基于深度学习的SISR方法着重于设计更深 /更广泛的模型，以学习低分辨率（LR）输入和高分辨率（HR）输出之间的非线性映射。这些现有的SR方法未考虑图像观察模型（物理）模型，因此需要大量网络的可训练参数，并具有大量的培训数据。为了解决这些问题，我们提出了一种深层迭代的超分辨率残差卷积网络（ISRRESCNET），该卷积网络（ISRRESCNET）通过以残留的学习方法以迭代方式训练深层网络来利用强大的图像正则化和大规模优化技术。各种超分辨率基准的广泛实验结果表明，与最先进的方法相比，我们使用一些可训练参数的方法改善了不同尺度因素的结果。
Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. The most recent deep learning based SISR methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and high-resolution (HR) outputs. These existing SR methods do not take into account the image observation (physical) model and thus require a large number of network's trainable parameters with a great volume of training data. To address these issues, we propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) that exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Extensive experimental results on various super-resolution benchmarks demonstrate that our method with a few trainable parameters improves the results for different scaling factors in comparison with the state-of-art methods.
