融合对比学习与生成对抗的Transformer模型，提升图像超分辨率质量。
CATformer: Contrastive Adversarial Transformer for Image Super-Resolution
- 双分支结构：主干扩散式Transformer逐步优化特征，辅分支增强抗噪对比能力。
- 在多个基准数据集上超越现有Transformer与扩散模型方法，重建质量更优。
- 适合关注图像修复与生成模型融合应用的研究者或工程师。
图像超分辨率仍是提升低分辨率图像质量的重要技术。本文提出CATformer（对比对抗变换器），一种结合扩散启发特征精炼、对抗学习与对比学习的新神经网络。CATformer采用双分支架构，主干为扩散启发的Transformer，逐步优化潜在表示；辅分支通过学习潜在对比度增强对噪声的鲁棒性。两者互补表征经融合后，由深层残差嵌套密集块解码，显著提升重建质量。大量实验表明，该模型在效率和视觉质量上均优于近期基于Transformer及扩散的方法。本工作弥合了变压器、扩散模型与GAN方法之间的性能差距，为扩散启发式变换器在超分辨率中的实际应用奠定基础。
原文摘要 · Abstract (English)
Super-resolution remains a promising technique to enhance the quality of low-resolution images. This study introduces CATformer (Contrastive Adversarial Transformer), a novel neural network integrating diffusion-inspired feature refinement with adversarial and contrastive learning. CATformer employs a dual-branch architecture combining a primary diffusion-inspired transformer, which progressively refines latent representations, with an auxiliary transformer branch designed to enhance robustness to noise through learned latent contrasts. These complementary representations are fused and decoded using deep Residual-in-Residual Dense Blocks for enhanced reconstruction quality. Extensive experiments on benchmark datasets demonstrate that CATformer outperforms recent transformer-based and diffusion-inspired methods both in efficiency and visual image quality. This work bridges the performance gap among transformer-, diffusion-, and GAN-based methods, laying a foundation for practical applications of diffusion-inspired transformers in super-resolution.
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