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

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

AIIA Lab, Harbin Institute of Technology.

paper | results | 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. 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

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

TODO

• Add implementation code
• Add pretrained model