用多分支空洞卷积提升图像超分辨率效率与质量
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
- 通过多分支空洞卷积扩大感受野，动态调制特征
- 在多个数据集上实现更高PSNR和SSIM，计算量更低
- 适合追求高效图像重建的轻量化模型开发者
在轻量级单图像超分辨率（SISR）中，重建质量与模型效率之间的平衡仍是关键挑战。尽管当前最先进的SISR方法普遍采用注意力机制来强调或抑制特征图，但其他架构范式仍值得探索。本文提出DiMoSR（Dilated Modulation Super-Resolution），一种通过特征调制增强表示能力的新架构，以补充注意力机制在轻量级SISR网络中的作用。该方法利用多分支空洞卷积，在保持计算效率的同时捕捉更丰富的上下文信息。实验表明，DiMoSR在多个基准数据集上优于现有轻量级方法，在达到更高PSNR和SSIM指标的同时，计算复杂度相当或更低。通过全面的消融实验，本工作不仅验证了DiMoSR的有效性，还揭示了注意力机制与特征调制之间的相互作用，为未来高效网络设计提供重要参考。代码与模型权重可在https://github.com/makinyilmaz/DiMoSR获取。
原文摘要 · Abstract (English)
Balancing reconstruction quality versus model efficiency remains a critical challenge in lightweight single image super-resolution (SISR). Despite the prevalence of attention mechanisms in recent state-of-the-art SISR approaches that primarily emphasize or suppress feature maps, alternative architectural paradigms warrant further exploration. This paper introduces DiMoSR (Dilated Modulation Super-Resolution), a novel architecture that enhances feature representation through modulation to complement attention in lightweight SISR networks. The proposed approach leverages multi-branch dilated convolutions to capture rich contextual information over a wider receptive field while maintaining computational efficiency. Experimental results demonstrate that DiMoSR outperforms state-of-the-art lightweight methods across diverse benchmark datasets, achieving superior PSNR and SSIM metrics with comparable or reduced computational complexity. Through comprehensive ablation studies, this work not only validates the effectiveness of DiMoSR but also provides critical insights into the interplay between attention mechanisms and feature modulation to guide future research in efficient network design. The code and model weights to reproduce our results are available at: https://github.com/makinyilmaz/DiMoSR
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