Abstract
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
Abstract (translated)
量化感知训练（QAT）与知识蒸馏（KD）的结合为模型压缩以便在边缘设备上部署带来了巨大的潜力。然而，针对对精度敏感的图像恢复（IR），即从退化图像中恢复视觉质量的整体优化问题仍然很大程度上未被探索。直接将QAT-KD应用于低级视觉揭示了三个关键瓶颈：教师-学生容量不匹配、解码器蒸馏过程中的空间误差放大以及由于量化噪声导致重建和蒸馏损失之间的“拉锯战”。为了应对这些问题，我们引入了量化的蒸馏恢复（QDR），这是一种面向边缘部署的IR框架。QDR通过FP32自我蒸馏消除容量差异，并通过无解码器蒸馏（DFD）防止误差放大，严格纠正网络瓶颈处的量化错误。为稳定优化“拉锯战”，我们提出了一种可学习幅度重新加权（LMR），动态地平衡竞争梯度。最后，我们设计了一个友好的边缘模型（EFM），该模型具有轻量级的学习退化门控（LDG）以动态调节空间退化定位。跨四个IR任务的广泛实验表明，我们的Int8模型恢复了96.5%的FP32性能，在NVIDIA Jetson Orin上实现了442帧每秒（FPS），并且提高了下游目标检测的16.3 mAP。 这种QDR框架通过引入一系列创新的技术和策略来克服传统方法中的瓶颈，显著提高了压缩模型在边缘设备上的部署效率与效果。
URL
https://arxiv.org/abs/2603.09624
