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[IEEE TIP] Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach

Gang Wu, Junjun Jiang, Junpeng Jiang, and Xianming Liu

AIIA Lab, Harbin Institute of Technology.

News

• 🚀 Accepted by IEEE TIP.
• Upload implementation codes
• Upload pretrained models

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. In particular, transformer-based methods, which leverage self-attention mechanisms, have led to significant breakthroughs but also introduce substantial computational costs. To tackle this issue, we introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR), offering an effective and efficient solution for lightweight image super-resolution. The proposed method inherits the advantages of both convolution-based and transformer-based approaches. Specifically, CFSR utilizes large kernel convolutions as a feature mixer to replace the self-attention module, efficiently modeling long-range dependencies and extensive receptive fields with minimal computational overhead. Furthermore, we propose an edge-preserving feed-forward network (EFN) designed to achieve local feature aggregation while effectively preserving high-frequency information. Extensive experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance compared to existing lightweight SR methods. When benchmarked against state-of-the-art methods such as ShuffleMixer, the proposed CFSR achieves a gain of 0.39 dB on the Urban100 dataset for the x2 super-resolution task while requiring 26% and 31% fewer parameters and FLOPs, respectively.

Results

Results of x2, x3, and x4 SR tasks are available at Google Drive

Citation

If you find this repository helpful, you may cite:

@ARTICLE{Wu_cfsr,
author={Wu, Gang and Jiang, Junjun and Junpeng Jiang and Liu, Xianming},
journal={IEEE Transactions on Image Processing}, 
title={Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach}, 
year={2024},
}

Acknowledgement

We thank the authors for their nice sharing of BasicSR, ECBSR, and ShuffleMixer