  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.
