Papers › Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach

Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach

11 Jan 2024arXiv:2401.05633archive 2025-07-28

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. 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. The code and pre-trained models are available at https://github.com/Aitical/CFSR.

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Code

aitical/cfsr officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Image Super-Resolution Super-Resolution

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Super-Resolution	Manga109 - 4x upscaling	CFSR	PSNR	30.72	#38 of 50	Archive leaderboard	report
Image Super-Resolution	Manga109 - 4x upscaling	CFSR	SSIM	0.9111	#38 of 50	Archive leaderboard	report
Image Super-Resolution	Set14 - 4x upscaling	CFSR	PSNR	28.73	#53 of 104	Archive leaderboard	report
Image Super-Resolution	Set14 - 4x upscaling	CFSR	SSIM	0.7842	#53 of 104	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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