Significant progress has been made in the field of super-resolution (SR), yet
many convolutional neural networks (CNNs) based SR models primarily focus on
restoring high-frequency details, often overlooking crucial low-frequency
contour information. Transformer-based SR methods, while incorporating global
structural details, frequently come with an abundance of parameters, leading to
high computational overhead. In this paper, we address these challenges by
introducing a Multi-Depth Branches Network (MDBN). This framework extends the
ResNet architecture by integrating an additional branch that captures vital
structural characteristics of images. Our proposed multi-depth branches module
(MDBM) involves the stacking of convolutional kernels of identical size at
varying depths within distinct branches. By conducting a comprehensive analysis
of the feature maps, we observe that branches with differing depths can extract
contour and detail information respectively. By integrating these branches, the
overall architecture can preserve essential low-frequency semantic structural
information during the restoration of high-frequency visual elements, which is
more closely with human visual cognition. Compared to GoogLeNet-like models,
our basic multi-depth branches structure has fewer parameters, higher
computational efficiency, and improved performance. Our model outperforms
state-of-the-art (SOTA) lightweight SR methods with less inference time. Our
code is available at this https URL

通过引入多深度分支网络（MDBN），本文提出了一种新的超分辨率方法，该方法可以更好地提取和保留高频细节同时降低计算代价。