  Deep convolutional neural networks (CNNs) have been widely applied for
low-level vision over the past five years. According to nature of different
applications, designing appropriate CNN architectures is developed. However,
customized architectures gather different features via treating all pixel
points as equal to improve the performance of given application, which ignores
the effects of local power pixel points and results in low training efficiency.
In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric
block (AB), a memory enhancement block (MEB) and a high-frequency feature
enhancement block (HFFEB) for image super-resolution. The AB utilizes
one-dimensional asymmetric convolutions to intensify the square convolution
kernels in horizontal and vertical directions for promoting the influences of
local salient features for SISR. The MEB fuses all hierarchical low-frequency
features from the AB via residual learning (RL) technique to resolve the
long-term dependency problem and transforms obtained low-frequency features
into high-frequency features. The HFFEB exploits low- and high-frequency
features to obtain more robust super-resolution features and address excessive
feature enhancement problem. Addditionally, it also takes charge of
reconstructing a high-resolution (HR) image. Extensive experiments show that
our ACNet can effectively address single image super-resolution (SISR), blind
SISR and blind SISR of blind noise problems. The code of the ACNet is shown at
https://github.com/hellloxiaotian/ACNet.
