让小模型同时学会去噪和修复，提升图像恢复效率。
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images
- 双教师架构：一个教去噪，一个教重建，学生同步学习。
- 压缩超80%计算量，修复效果仍保持领先水平。
- 适合部署在资源受限设备上的图像修复任务。
通过知识蒸馏进行模型压缩在分类和分割任务中已广泛应用，但在图像到图像翻译，特别是图像恢复领域仍研究不足。为此，我们提出一种面向图像恢复模型压缩的并行学习知识蒸馏框架（SLKD）。该框架采用双教师、单学生结构，包含两种学习策略：降质去除学习（DRL）与图像重建学习（IRL）。在DRL中，学生编码器从教师A学习，通过新颖的BRISQUE提取器引导，聚焦于去除退化因素；在IRL中，学生解码器从教师B学习，借助提出的PIQE提取器辅助重建清晰图像。两种策略使学生能同时从退化和干净图像中学习，实现高质量模型压缩。在五个数据集和三个任务上的实验表明，SLKD在参数量和浮点运算量上均减少超过80%，同时保持优异的图像恢复性能。
原文摘要 · Abstract (English)
Model compression through knowledge distillation has seen extensive application in classification and segmentation tasks. However, its potential in image-to-image translation, particularly in image restoration, remains underexplored. To address this gap, we propose a Simultaneous Learning Knowledge Distillation (SLKD) framework tailored for model compression in image restoration tasks. SLKD employs a dual-teacher, single-student architecture with two distinct learning strategies: Degradation Removal Learning (DRL) and Image Reconstruction Learning (IRL), simultaneously. In DRL, the student encoder learns from Teacher A to focus on removing degradation factors, guided by a novel BRISQUE extractor. In IRL, the student decoder learns from Teacher B to reconstruct clean images, with the assistance of a proposed PIQE extractor. These strategies enable the student to learn from degraded and clean images simultaneously, ensuring high-quality compression of image restoration models. Experimental results across five datasets and three tasks demonstrate that SLKD achieves substantial reductions in FLOPs and parameters, exceeding 80\%, while maintaining strong image restoration performance.
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