PocketDVDNet: Realtime Video Denoising for Real Camera Noise
Crispian Morris, Imogen Dexter, Fan Zhang, David R. Bull, Nantheera Anantrasirichai

TL;DR
PocketDVDNet is a lightweight, real-time video denoising model that effectively handles realistic camera noise through model compression, domain-specific training, and knowledge distillation, achieving high quality with significantly reduced size.
Contribution
The paper introduces PocketDVDNet, a novel, highly compressed video denoising network that leverages physics-informed noise modeling and domain-adapted distillation for real-time performance.
Findings
Reduces model size by 74%
Achieves real-time processing of 5-frame patches
Improves denoising quality over baseline models
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…
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Taxonomy
TopicsImage and Signal Denoising Methods · Sparse and Compressive Sensing Techniques · Image Enhancement Techniques
