  In recent years, single image super-resolution (SR) methods based on deep
convolutional neural networks (CNNs) have made significant progress. However,
due to the non-adaptive nature of the convolution operation, they cannot adapt
to various characteristics of images, which limits their representational
capability and, consequently, results in unnecessarily large model sizes. To
address this issue, we propose a novel multi-path adaptive modulation network
(MAMNet). Specifically, we propose a multi-path adaptive modulation block
(MAMB), which is a lightweight yet effective residual block that adaptively
modulates residual feature responses by fully exploiting their information via
three paths. The three paths model three types of information suitable for SR:
1) channel-specific information (CSI) using global variance pooling, 2)
inter-channel dependencies (ICD) based on the CSI, 3) and channel-specific
spatial dependencies (CSD) via depth-wise convolution. We demonstrate that the
proposed MAMB is effective and parameter-efficient for image SR than other
feature modulation methods. In addition, experimental results show that our
MAMNet outperforms most of the state-of-the-art methods with a relatively small
number of parameters.
