Cascaded UNet for progressive noise residual prediction for structure-preserving video denoising

• Published in: Computer vision and image understanding Vol. 248; p. 104103
• Main Authors: Pimpale, Abhijeet, Bhurchandi, Kishor
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2024
• Subjects: Cascaded UNet; Deep learning; Residue modelling; SSIM; Video denoising; Deep learning; Video denoising; Residue modelling; SSIM; Cascaded UNet
• ISSN: 1077-3142
• DOI: 10.1016/j.cviu.2024.104103

The prominence of high-quality video services has become so substantial that by 2030, it is estimated that approximately 80% of internet traffic will consist of videos. On the contrary, video denoising remains a relatively unexplored and intricate field, presenting more substantial challenges compared to image denoising. Many published deep learning video denoising algorithms typically rely on simple, efficient single encoder–decoder networks, but they have inherent limitations in preserving intricate image details and effectively managing noise information propagation for noise residue modelling. In response to these challenges, the proposed work introduces an innovative approach; in terms of utilization of cascaded UNets for progressive noise residual prediction in video denoising. This multi-stage encoder–decoder architecture is meticulously designed to accurately predict noise residual maps, thereby preserving the locally fine details within video content as represented by SSIM. The proposed network has undergone extensive end-to-end training from scratch without explicit motion compensation to reduce complexity. In terms of the more rigorous SSIM metric, the proposed network outperformed all video denoising methods while maintaining a comparable PSNR. •Novel cascaded UNet for noise prediction, ensuring superior video denoising.•Multistage encoder–decoder enhances noise reduction and preserves video details.•Unique method for managing noise information progressively.•Improved SSIM over traditional methods, ensuring detail-preserving video denoising.