In recent years, Convolutional Neural network (CNN) has achieved great success in the field of Single-Image SuperResolution (SISR) tasks. In order to improve the SISR performance, this paper proposes an accurate SISR method by introducing cascading dense connections in a very deep CNN. In detail, we construct the Cascading Dense Network (CDN) to fully make use of the features from input low resolution image and all the convolutional layers, which implements a cascading mechanism upon the dense connected convolutional layers. In addition, the global feature fusion in the CDN enables both short- and long- paths to be built directly connection from the input to each layer, alleviating the vanishing-gradient problem of very deep CNN. Extensive experiments show that our CDN achieves state-of-the-art performance on traditional SISR metrics (i.e. PSNR and SSIM). In addition, we introduce the object recognition as the additional evaluation metric for SISR, which further demonstrates the effectiveness of our method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.