Live video denoising under realistic, multi-component sensor noise remains challenging for applications such as autofocus, autonomous driving, and surveillance. We propose PocketDVDNet, a lightweight video denoiser developed using our model compression framework that combines sparsity-guided structured pruning, a physics-informed noise model, and knowledge distillation to achieve high-quality restoration with reduced resource demands. Starting from a reference model, we induce sparsity, apply targeted channel pruning, and retrain a teacher on realistic multi-component noise. The student network learns implicit noise handling, eliminating the need for explicit noise-map inputs. PocketDVDNet reduces the original model size by 74% while improving denoising quality and processing 5-frame patches in real-time. These results demonstrate that aggressive compression, combined with domain-adapted distillation, can reconcile performance and efficiency for practical, real-time video denoising.

翻译：在真实多分量传感器噪声条件下进行实时视频去噪，对于自动对焦、自动驾驶和监控等应用仍具挑战性。我们提出PocketDVDNet，这是一种通过模型压缩框架开发的轻量级视频去噪器，该框架结合了稀疏性引导的结构化剪枝、物理信息噪声模型和知识蒸馏，以在降低资源需求的同时实现高质量复原。我们从参考模型出发，引入稀疏性，应用针对性通道剪枝，并在真实多分量噪声上重新训练教师网络。学生网络学习隐式噪声处理，从而无需显式的噪声图输入。PocketDVDNet将原始模型大小减少了74%，同时提升了去噪质量，并能实时处理5帧图像块。这些结果表明，激进的压缩结合领域适应的蒸馏，能够为实际应用的实时视频去噪在性能与效率之间取得平衡。