A Comparative Study of NAFNet Baselines for Image Restoration

2025-06-24

AI Summary

This study systematically evaluates NAFNet’s performance in image denoising and deblurring, focusing on the functional mechanisms of its core components: SimpleGate activation, Simplified Channel Attention (SCA), and LayerNorm. Through controlled ablation experiments on CIFAR-10, we quantitatively demonstrate that SimpleGate substantially outperforms conventional activations (e.g., ReLU), SCA maintains effective attention modeling while reducing parameter count, and LayerNorm significantly enhances training stability and convergence speed. Joint integration of these components yields consistent improvements—1.2–2.3 dB PSNR gain and 0.015–0.028 SSIM increase—over baseline models. To our knowledge, this is the first work to disentangle and quantify the individual contributions of NAFNet’s lightweight architectural elements. Our findings provide empirical guidance for designing efficient, stable image restoration networks, offering concrete evidence for component-level architectural decisions in practical deployment scenarios.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• Evaluates NAFNet's effectiveness in restoring noisy, blurred images
• Compares core components like SimpleGate, SCA, and LayerNorm
• Identifies optimal design choices for image restoration models

Innovation

• SimpleGate activation for better performance
• Simplified Channel Attention mechanism
• LayerNorm ensures stable training