Recently, great progress has been made in single-image super-resolution
(SISR) based on deep learning technology. However, the existing methods usually
require a large computational cost. Meanwhile, the activation function will
cause some features of the intermediate layer to be lost. Therefore, it is a
challenge to make the model lightweight while reducing the impact of
intermediate feature loss on the reconstruction quality. In this paper, we
propose a Feature Interaction Weighted Hybrid Network (FIWHN) to alleviate the
above problem. Specifically, FIWHN consists of a series of novel Wide-residual
Distillation Interaction Blocks (WDIB) as the backbone, where every third WDIBs
form a Feature shuffle Weighted Group (FSWG) by mutual information mixing and
fusion. In addition, to mitigate the adverse effects of intermediate feature
loss on the reconstruction results, we introduced a well-designed Wide
Convolutional Residual Weighting (WCRW) and Wide Identical Residual Weighting
(WIRW) units in WDIB, and effectively cross-fused features of different
finenesses through a Wide-residual Distillation Connection (WRDC) framework and
a Self-Calibrating Fusion (SCF) unit. Finally, to complement the global
features lacking in the CNN model, we introduced the Transformer into our model
and explored a new way of combining the CNN and Transformer. Extensive
quantitative and qualitative experiments on low-level and high-level tasks show
that our proposed FIWHN can achieve a good balance between performance and
efficiency, and is more conducive to downstream tasks to solve problems in
low-pixel scenarios.

本文介绍了一种用于单张图像超分辨率重建的神经网络模型 FIWHN，通过将 WDIB 组成 FSWG、WCRW 和 WIRW 单元、WRDC 框架和 SCF 单元等方式，解决了模型复杂度高、中间特征丢失等问题，并引入了 Transformer，实现了 CNN 和 Transformer 的新型组合，结果表明 FIWHN 能够在性能和效率之间取得良好的平衡，更有利于下游任务解决低像素场景中的问题。