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arXiv · 2601.16780

PocketDVDNet: Realtime Video Denoising for Real Camera Noise

Crispian Morris·Imogen Dexter·Fan Zhang·David R. Bull·Nantheera Anantrasirichai
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Abstract

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.

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Crispian Morris, Imogen Dexter, Fan Zhang, David R. Bull, Nantheera Anantrasirichai. 2026-01-23. PocketDVDNet: Realtime Video Denoising for Real Camera Noise. https://arxiv.org/abs/2601.16780

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