Video Denoising Using Cascaded Skip Connection Feedforward UNets

Quality video services have already gained high technical and commercial importance. The published work so far in this domain proposed mathematically and computationally complex algorithms, followed by the recent training-greedy deep learning-based denoising algorithms. This work proposes a video-de...

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Bibliographic Details
Main Authors: Pimpale, Abhijeet M., Bhurchandi, Kishor M.
Format: Conference Proceeding
Language:English
Subjects:
Complexity theory
Estimation
Gaussian noise
IEEE Regions
Motion compensation
Noise measurement
Noise reduction
Online Access:Request full text
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Summary:Quality video services have already gained high technical and commercial importance. The published work so far in this domain proposed mathematically and computationally complex algorithms, followed by the recent training-greedy deep learning-based denoising algorithms. This work proposes a video-denoising algorithm based on multiple UNet networks. The proposed video-denoising algorithm uses multiple encoder-decoder networks for video noise residual frame estimation, un-like the single encoder-decoder used by the published denoising algorithms. Using multiple skip connection UNets, we increase the residual noise modeling accuracy while restricting the signal features, which helps to improve denoising performance. The proposed network is trained end-to-end without motion compensation to reduce its complexity. The proposed network outperforms all the video denoising algorithms in terms of SSIM metric while it yields comparable performance in terms of PSNR.
ISSN:2159-3450
DOI:10.1109/TENCON58879.2023.10322505