轻量级去噪网络，用物理模拟数据提升手机端去噪效果
LiteKD-Net: Lightweight Knowledge-Distilled Network for Mobile Image Denoising
- 用物理引导的噪声模拟生成训练数据，减少真实配对数据依赖
- 基于深度可分离卷积设计轻量学生模型，推理速度提升显著
- 特征级知识蒸馏无额外开销，兼顾效果与计算效率
移动端图像去噪需兼顾优异恢复质量与低计算成本，且大规模低质-干净图像对收集困难。为此，我们提出 LiteKD-Net，一种面向移动端的轻量级知识蒸馏去噪网络。首先，构建基于像素串扰的物理引导噪声模拟流程，生成配对训练数据；其次，将 Real-ESRGAN 改造为无分辨率变化的去噪模型，并基于深度可分离卷积设计轻量级学生网络（Lite-RRDB 块）；第三，采用特征级知识蒸馏，将教师模型的恢复能力迁移至学生模型，不增加推理开销。在真实世界数据集上的实验表明，该模型在保持良好恢复质量的同时，显著降低运行时长并提升推理速率。相比 SwinIR，本方法在各项指标上均表现最佳，验证了其在恢复质量与计算效率之间的出色权衡。
原文摘要 · Abstract (English)
Mobile image denoising requires both good restoration quality and low computational cost. In addition, it's annoying to collect large-scale LQ-GT clean pairs. As a result, we propose LiteKD-Net, a lightweight knowledge-distilled network for mobile image denoising. First, a physics-guided noise simulation pipeline generates paired training data by adding pixel crosstalk compared with pipelines applied to cameras. Next, we adapt the Real-ESRGAN to identity-resolution denoising and construct a lightweight Student using Lite-RRDB blocks based on depthwise separable convolutions. Third, feature-level knowledge distillation is applied to transfer the Teacher's restoration capability to the Student without introducing additional inference cost. Experiments on real-world datasets show that our model reaches great reduction in runtime and increase in the inference rate with good restoration quality. Our model also reaches the best in all metrics compared with SwinIR. These results indicate that LiteKD-Net provides a great trade-off between restoration quality and computational efficiency.
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