论文标题
图像超分辨率的级联卷积神经网络
Cascade Convolutional Neural Network for Image Super-Resolution
论文作者
论文摘要
随着超分辨率卷积神经网络（SRCNN）的发展，深度学习技术已被广泛应用于图像超分辨率领域。先前的作品主要集中于优化SRCNN的结构，SRCNN的结构在速度和恢复质量方面的性能良好，以实现图像超分辨率。但是，这些方法中的大多数仅在训练过程中考虑特定的比例图像，同时忽略了不同图像比例之间的关系。在本文中，我们提出了一个级联的卷积神经网络，用于图像超分辨率（CSRCNN），其中包括三个级联的快速SRCNN，每个快速srcnn都可以处理特定的比例图像。可以同时训练不同量表的图像，并且学习的网络可以充分利用以不同图像尺度列出的信息。广泛的实验表明，我们的网络可以实现图像SR的良好性能。
With the development of the super-resolution convolutional neural network (SRCNN), deep learning technique has been widely applied in the field of image super-resolution. Previous works mainly focus on optimizing the structure of SRCNN, which have been achieved well performance in speed and restoration quality for image super-resolution. However, most of these approaches only consider a specific scale image during the training process, while ignoring the relationship between different scales of images. Motivated by this concern, in this paper, we propose a cascaded convolution neural network for image super-resolution (CSRCNN), which includes three cascaded Fast SRCNNs and each Fast SRCNN can process a specific scale image. Images of different scales can be trained simultaneously and the learned network can make full use of the information resided in different scales of images. Extensive experiments show that our network can achieve well performance for image SR.
