Transforming Image Super-Resolution: A ConvFormer-based Efficient
  Approach
Gang Wu Junjun Jiang Junpeng Jiang Xianming Liu
Recent progress in single-image super-resolution (SISR) has achieved
remarkable performance, yet the computational costs of these methods remain a
challenge for deployment on resource-constrained devices. Especially for
transformer-based methods, the self-attention mechanism in such models brings
great breakthroughs while incurring substantial computational costs. To tackle
this issue, we introduce the Convolutional Transformer layer (ConvFormer) and
the ConvFormer-based Super-Resolution network (CFSR), which offer an effective
and efficient solution for lightweight image super-resolution tasks. In detail,
CFSR leverages the large kernel convolution as the feature mixer to replace the
self-attention module, efficiently modeling long-range dependencies and
extensive receptive fields with a slight computational cost. Furthermore, we
propose an edge-preserving feed-forward network, simplified as EFN, to obtain
local feature aggregation and simultaneously preserve more high-frequency
information. Extensive experiments demonstrate that CFSR can achieve an
advanced trade-off between computational cost and performance when compared to
existing lightweight SR methods. Compared to state-of-the-art methods, e.g.
ShuffleMixer, the proposed CFSR achieves 0.39 dB gains on Urban100 dataset for
x2 SR task while containing 26% and 31% fewer parameters and FLOPs,
respectively. Code and pre-trained models are available at
https://github.com/Aitical/CFSR.