Accurate single image super-resolution using cascading dense connections

Wei Wei; Guoqi Feng; Dongliang Cui

doi:10.1117/12.2540083

14 August 2019 Accurate single image super-resolution using cascading dense connections

Wei Wei, Guoqi Feng, Dongliang Cui

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790Z (2019) https://doi.org/10.1117/12.2540083
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

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.

Citation Download Citation

Wei Wei, Guoqi Feng, and Dongliang Cui "Accurate single image super-resolution using cascading dense connections", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790Z (14 August 2019); https://doi.org/10.1117/12.2540083

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KEYWORDS

Convolution

Lawrencium

Super resolution

Convolutional neural networks

Object recognition

Visualization

Feature extraction

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