Compact Cross-Reparam Convolution Network for Efficient Image Super-resolution
Eaven Huang, Runan Wang, Yifan Wang, Tuo Leng · 2023
With increasing demand for deployment on edge platforms, there is a need for efficient super-resolution methods. Linear overparameterization is widely used to enhance lightweight model performance. However, the general usage of the reparameterization technique is merging multi-branch into standard 3 × 3 convolutions, no one explores its usage in 1D-convolution, which is energy efficiency and adepts at capturing structural edge features. We propose a compact cross-reparam convolution super-resolution (CRCSR) network in a vgg-style plain architecture. We design Cross-Reparam convolution (CRC) to enhance feature representation for cross-convolution. We leverage a sparse large kernel to capture long-range information while limiting the computational costs and number of parameters. Besides, when training deeper type CRCSR, reserving and merging operations are used to build Rmparam-Cross Block (RCB) to improve the convergence of deeper networks. Experiments on super-resolution datasets of different sizes demonstrate that CRCSR has competitive restoration performance compared to state-of-the-art lightweight SR models