论文标题
通过知识蒸馏的单图降解和重建网络的多重降解和重建网络
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
论文作者
论文摘要
随着深度学习的发展，单图像denoising（SID）取得了重大突破。但是，所提出的方法通常伴随着大量参数，这极大地限制了其应用程序方案。与以前盲目增加网络深度的作品不同，我们探索了嘈杂图像的降解机制，并提出了轻巧的多重降解和重建网络（MDRN），以逐步消除噪声。同时，我们提出了两种新型的异质知识蒸馏策略（HMD），以使MDRN从异质模型中学习更丰富，更准确的特征，这使得在极端条件下重建更高质量的demeno图像是可能的。广泛的实验表明，我们的MDRN对其他参数较少的SID模型实现了有利的性能。同时，大量的消融研究表明，引入的HMD可以在高噪声水平下改善微型模型或模型的性能，这对于相关应用非常有用。
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of parameters, which greatly limits their application scenarios. Different from previous works that blindly increase the depth of the network, we explore the degradation mechanism of the noisy image and propose a lightweight Multiple Degradation and Reconstruction Network (MDRN) to progressively remove noise. Meanwhile, we propose two novel Heterogeneous Knowledge Distillation Strategies (HMDS) to enable MDRN to learn richer and more accurate features from heterogeneous models, which make it possible to reconstruct higher-quality denoised images under extreme conditions. Extensive experiments show that our MDRN achieves favorable performance against other SID models with fewer parameters. Meanwhile, plenty of ablation studies demonstrate that the introduced HMDS can improve the performance of tiny models or the model under high noise levels, which is extremely useful for related applications.
