  CNNs with strong learning abilities are widely chosen to resolve
super-resolution problem. However, CNNs depend on deeper network architectures
to improve performance of image super-resolution, which may increase
computational cost in general. In this paper, we present an enhanced
super-resolution group CNN (ESRGCNN) with a shallow architecture by fully
fusing deep and wide channel features to extract more accurate low-frequency
information in terms of correlations of different channels in single image
super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is
useful to inherit more long-distance contextual information for resolving
long-term dependency. An adaptive up-sampling operation is gathered into a CNN
to obtain an image super-resolution model with low-resolution images of
different sizes. Extensive experiments report that our ESRGCNN surpasses the
state-of-the-arts in terms of SISR performance, complexity, execution speed,
image quality evaluation and visual effect in SISR. Code is found at
https://github.com/hellloxiaotian/ESRGCNN.
