  Deep Neural Network (DNN) based super-resolution algorithms have greatly
improved the quality of the generated images. However, these algorithms often
yield significant artifacts when dealing with real-world super-resolution
problems due to the difficulty in learning misaligned optical zoom. In this
paper, we introduce a Squared Deformable Alignment Network (SDAN) to address
this issue. Our network learns squared per-point offsets for convolutional
kernels, and then aligns features in corrected convolutional windows based on
the offsets. So the misalignment will be minimized by the extracted aligned
features. Different from the per-point offsets used in the vanilla Deformable
Convolutional Network (DCN), our proposed squared offsets not only accelerate
the offset learning but also improve the generation quality with fewer
parameters. Besides, we further propose an efficient cross packing attention
layer to boost the accuracy of the learned offsets. It leverages the packing
and unpacking operations to enlarge the receptive field of the offset learning
and to enhance the ability of extracting the spatial connection between the
low-resolution images and the referenced images. Comprehensive experiments show
the superiority of our method over other state-of-the-art methods in both
computational efficiency and realistic details.
