  Image restoration aims to recover high-quality images from their corrupted
counterparts. Many existing methods primarily focus on the spatial domain,
neglecting the understanding of frequency variations and ignoring the impact of
implicit noise in skip connections. In this paper, we introduce a multi-scale
frequency selection network (MSFSNet) that seamlessly integrates spatial and
frequency domain knowledge, selectively recovering richer and more accurate
information. Specifically, we initially capture spatial features and input them
into dynamic filter selection modules (DFS) at different scales to integrate
frequency knowledge. DFS utilizes learnable filters to generate high and
low-frequency information and employs a frequency cross-attention mechanism
(FCAM) to determine the most information to recover. To learn a multi-scale and
accurate set of hybrid features, we develop a skip feature fusion block (SFF)
that leverages contextual features to discriminatively determine which
information should be propagated in skip-connections. It is worth noting that
our DFS and SFF are generic plug-in modules that can be directly employed in
existing networks without any adjustments, leading to performance improvements.
Extensive experiments across various image restoration tasks demonstrate that
our MSFSNet achieves performance that is either superior or comparable to
state-of-the-art algorithms.
