对比改进版NAFNet在图像修复中的表现，验证关键模块有效性。
A Comparative Study of NAFNet Baselines for Image Restoration
- 采用SimpleGate与简化注意力机制，替代传统激活和注意力。
- 在噪声模糊图像上，PSNR与SSIM均优于基线模型。
- 适合图像修复研究者参考模块设计与训练稳定性优化。
我们研究了NAFNet（非线性激活自由网络），一种用于图像修复的简单高效深度学习基线。通过使用带有噪声和模糊的CIFAR10图像，我们对NAFNet的核心组件进行了消融实验。基线模型包含SimpleGate激活、简化通道注意力（SCA）和层归一化（LayerNormalization）。我们将该基线与移除或替换部分组件的不同变体进行比较。定量结果（PSNR、SSIM）及示例表明，每个修改对修复性能的影响。研究发现支持原始设计：SimpleGate与简化注意力机制优于传统激活与注意力，而层归一化对稳定训练至关重要。最后提出模型设计建议，讨论潜在改进与未来工作。
原文摘要 · Abstract (English)
We study NAFNet (Nonlinear Activation Free Network), a simple and efficient deep learning baseline for image restoration. By using CIFAR10 images corrupted with noise and blur, we conduct an ablation study of NAFNet's core components. Our baseline model implements SimpleGate activation, Simplified Channel Activation (SCA), and LayerNormalization. We compare this baseline to different variants that replace or remove components. Quantitative results (PSNR, SSIM) and examples illustrate how each modification affects restoration performance. Our findings support the NAFNet design: the SimpleGate and simplified attention mechanisms yield better results than conventional activations and attention, while LayerNorm proves to be important for stable training. We conclude with recommendations for model design, discuss potential improvements, and future work.
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