Real-World Video Denoising for Visual Inspection in High-Dose Radiological Environments

Young, Andrew and Fei, Zhouxiang and Zabalza, Jaime and West, Graeme and Murray, Paul and McArthur, Stephen (2024) Real-World Video Denoising for Visual Inspection in High-Dose Radiological Environments. Nuclear Technology. ISSN 0029-5450

Text Real-World Video Denoising for Visual Inspection in High-Dose Radiological Environments.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (8MB)

Abstract

In high-dose radiological environments, where precision and safety are of utmost importance, the ability to acquire accurate and clear visual information is of paramount importance for ensuring safety and reliability in critical industrial processes. However, these environments inherently introduce significant challenges due to the adverse effects of radiation on imaging equipment. As a consequence, inspection videos captured within such high-radiation environments often contain a significant amount of noise. This noise substantially complicates the task of identifying and assessing potential defects in vital components. It also diverts attention and resources toward investigating false positives created by noise, leading to inefficiencies, and for industrial processes on the critical path, this can further prolong the outage. Addressing this noise is essential not only for precision, but also for ensuring safety, reliability, and efficiency in critical industrial processes.

In this paper, we present a custom-designed filter utilizing a priori information about camera position and trajectory to remove the noise from the inspection videos, making the defects easier to manually identify. As the camera movement is in one direction at a constant speed, the proposed approach uses this temporal and spatial information to accurately remove the noise. This approach applies to a subset of visual inspection problems throughout the nuclear industry, as well as many other industries where there is knowledge available about the camera speed and direction of travel. The proposed approach is compared with three accepted/well-known approaches, median filtering, bilateral filtering, and fast nonlocal means denoising, and an additional state-of-the-art deep learning model is also used for comparison. It was found that the proposed approach produces the most accurate video denoising in terms of visual quality and the retainment of the defect features throughout the videos tested.

Item Type:	Article
Additional Information and Comments:	© 2024 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Faculty / Department:	Faculty of Human and Digital Sciences > School of Computer Science and the Environment
Depositing User:	Zhouxiang Fei
Date Deposited:	19 Feb 2025 11:17
Last Modified:	19 Feb 2025 11:17
URI:	https://hira.hope.ac.uk/id/eprint/4599

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