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.