  Recent research on deep convolutional neural networks (CNNs) has provided a
significant performance boost on efficient super-resolution (SR) tasks by
trading off the performance and applicability. However, most existing methods
focus on subtracting feature processing consumption to reduce the parameters
and calculations without refining the immediate features, which leads to
inadequate information in the restoration. In this paper, we propose a
lightweight network termed DDistill-SR, which significantly improves the SR
quality by capturing and reusing more helpful information in a static-dynamic
feature distillation manner. Specifically, we propose a plug-in reparameterized
dynamic unit (RDU) to promote the performance and inference cost trade-off.
During the training phase, the RDU learns to linearly combine multiple
reparameterizable blocks by analyzing varied input statistics to enhance
layer-level representation. In the inference phase, the RDU is equally
converted to simple dynamic convolutions that explicitly capture robust dynamic
and static feature maps. Then, the information distillation block is
constructed by several RDUs to enforce hierarchical refinement and selective
fusion of spatial context information. Furthermore, we propose a dynamic
distillation fusion (DDF) module to enable dynamic signals aggregation and
communication between hierarchical modules to further improve performance.
Empirical results show that our DDistill-SR outperforms the baselines and
achieves state-of-the-art results on most super-resolution domains with much
fewer parameters and less computational overhead. We have released the code of
DDistill-SR at https://github.com/icandle/DDistill-SR.
