  Magnetic resonance imaging plays an essential role in clinical diagnosis by
acquiring the structural information of biological tissue. Recently, many
multi-contrast MRI super-resolution networks achieve good effects. However,
most studies ignore the impact of the inappropriate foreground scale and patch
size of multi-contrast MRI, which probably leads to inappropriate feature
alignment. To tackle this problem, we propose the Flexible Alignment
Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution.
The Flexible Alignment module of FASR-Net consists of two modules for feature
alignment. (1) The Single-Multi Pyramid Alignment(S-A) module solves the
situation where low-resolution (LR) images and reference (Ref) images have
different scales. (2) The Multi-Multi Pyramid Alignment(M-A) module solves the
situation where LR and Ref images have the same scale. Besides, we propose the
Cross-Hierarchical Progressive Fusion (CHPF) module aiming at fusing the
features effectively, further improving the image quality. Compared with other
state-of-the-art methods, FASR-net achieves the most competitive results on
FastMRI and IXI datasets. Our code will be available at
\href{https://github.com/yimingliu123/FASR-Net}{https://github.com/yimingliu123/FASR-Net}.
