  A longstanding challenge in Super-Resolution (SR) is how to efficiently
enhance high-frequency details in Low-Resolution (LR) images while maintaining
semantic coherence. This is particularly crucial in practical applications
where SR models are often deployed on low-power devices. To address this issue,
we propose an innovative asymmetric SR architecture featuring Multi-Depth
Branch Module (MDBM). These MDBMs contain branches of different depths,
designed to capture high- and low-frequency information simultaneously and
efficiently. The hierarchical structure of MDBM allows the deeper branch to
gradually accumulate fine-grained local details under the contextual guidance
of the shallower branch. We visualize this process using feature maps, and
further demonstrate the rationality and effectiveness of this design using
proposed novel Fourier spectral analysis methods. Moreover, our model exhibits
more significant spectral differentiation between branches than existing branch
networks. This suggests that MDBM reduces feature redundancy and offers a more
effective method for integrating high- and low-frequency information. Extensive
qualitative and quantitative evaluations on various datasets show that our
model can generate structurally consistent and visually realistic HR images. It
achieves state-of-the-art (SOTA) results at a very fast inference speed. Our
code is available at https://github.com/thy960112/MDBN.
