PocketDVDNet: Realtime Video Denoising for Real Camera Noise

• URL: http://arxiv.org/abs/2601.16780v1
• Date: Fri, 23 Jan 2026 14:27:03 GMT
• Title: PocketDVDNet: Realtime Video Denoising for Real Camera Noise
• Authors: Crispian Morris, Imogen Dexter, Fan Zhang, David R. Bull, Nantheera Anantrasirichai,
• Abstract summary: We propose PocketDVDNet, a lightweight video denoiser developed using our model compression framework.<n>We induce sparsity, apply targeted channel pruning, and retrain a teacher on realistic multi-component noise.<n>PocketDVDNet reduces the original model size by 74% while improving denoising quality and processing 5-frame patches in real-time.
• Score: 7.3429091913205164
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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