Quantization-Aware Training (QAT), combined with Knowledge Distillation (KD), holds immense promise for compressing models for edge deployment. However, joint optimization for precision-sensitive image restoration (IR) to recover visual quality from degraded images remains largely underexplored. Directly adapting QAT-KD to low-level vision reveals three critical bottlenecks: teacher-student capacity mismatch, spatial error amplification during decoder distillation, and an optimization "tug-of-war" between reconstruction and distillation losses caused by quantization noise. To tackle these, we introduce Quantization-aware Distilled Restoration (QDR), a framework for edge-deployed IR. QDR eliminates capacity mismatch via FP32 self-distillation and prevents error amplification through Decoder-Free Distillation (DFD), which corrects quantization errors strictly at the network bottleneck. To stabilize the optimization tug-of-war, we propose a Learnable Magnitude Reweighting (LMR) that dynamically balances competing gradients. Finally, we design an Edge-Friendly Model (EFM) featuring a lightweight Learnable Degradation Gating (LDG) to dynamically modulate spatial degradation localization. Extensive experiments across four IR tasks demonstrate that our Int8 model recovers 96.5% of FP32 performance, achieves 442 frames per second (FPS) on an NVIDIA Jetson Orin, and boosts downstream object detection by 16.3 mAP

翻译：量化感知训练（QAT）与知识蒸馏（KD）相结合，为边缘部署的模型压缩带来了巨大前景。然而，针对精度敏感的图像复原任务，通过联合优化从退化图像中恢复视觉质量的研究仍相对不足。直接将QAT-KD应用于低级视觉任务时，暴露出三个关键瓶颈：师生模型容量不匹配、解码器蒸馏过程中的空间误差放大，以及由量化噪声引起的重建损失与蒸馏损失之间的优化“拉锯战”。为解决这些问题，我们提出了量化感知蒸馏复原框架，这是一种面向边缘部署图像复原的解决方案。该框架通过FP32自蒸馏消除容量不匹配问题，并采用解码器无关蒸馏方法防止误差放大——该方法仅在网络瓶颈处严格校正量化误差。为稳定优化过程中的拉锯现象，我们提出了可学习幅度重加权机制，动态平衡竞争梯度。最后，我们设计了边缘友好模型，其轻量级可学习退化门控模块能够动态调节空间退化定位。在四个图像复原任务上的大量实验表明，我们的Int8模型恢复了FP32模型96.5%的性能，在NVIDIA Jetson Orin平台上达到每秒442帧的处理速度，并将下游目标检测任务的性能提升了16.3 mAP。