Gang Wu, Junjun Jiang, Junpeng Jiang, and Xianming Liu
AIIA Lab, Harbin Institute of Technology.
This repository is the official PyTorch implementation of "Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach"
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 \textit{0.39 dB} gains on Urban100 dataset for
$\times2$ SR task while containing\textit{ 26% }and \textit{31%} fewer parameters and FLOPs, respectively.
Results of x2, x3, and x4 SR tasks are available at Google Drive
Method | Scale | Params | FLOPs | Set5 (PSNR/SSIM) | Set14 (PSNR/SSIM) | B100 (PSNR/SSIM) | Urban100 (PSNR/SSIM) | Manga109 (PSNR/SSIM) |
---|---|---|---|---|---|---|---|---|
VDSR | ||||||||
LapSRN | ||||||||
IDN | ||||||||
CARN | ||||||||
SRResNet | ||||||||
IMDN | ||||||||
LatticeNet | ||||||||
LAPAR-A | ||||||||
SMSR | ||||||||
ECBSR | ||||||||
PAN | ||||||||
DRSAN | ||||||||
DDistill-SR | ||||||||
RFDN | ||||||||
ShuffleMixer | ||||||||
CFSR (Ours) |
- Add implementation code
- Add pretrained model