  Recently, deep convolutional neural network methods have achieved an
excellent performance in image superresolution (SR), but they can not be easily
applied to embedded devices due to large memory cost. To solve this problem, we
propose a pyramidal dense attention network (PDAN) for lightweight image
super-resolution in this paper. In our method, the proposed pyramidal dense
learning can gradually increase the width of the densely connected layer inside
a pyramidal dense block to extract deep features efficiently. Meanwhile, the
adaptive group convolution that the number of groups grows linearly with dense
convolutional layers is introduced to relieve the parameter explosion. Besides,
we also present a novel joint attention to capture cross-dimension interaction
between the spatial dimensions and channel dimension in an efficient way for
providing rich discriminative feature representations. Extensive experimental
results show that our method achieves superior performance in comparison with
the state-of-the-art lightweight SR methods.
