Deep learning-based RGB-thermal image denoising: review and applications
Multimedia Tools and Applications, March 2024
Yan Yu, Boon Giin Lee, Matthew Pike, Qian Zhang, Wan-Young Chung. 2024. Deep learning-based RGB-thermal image denoising: review and applications. In Multimedia Tools and Applications. DOI:https://doi.org/10.1007/s11042-023-15916-7
Yan Yu and Boon Giin Lee and Matthew Pike and Qian Zhang and Wan-Young Chung. (2024). Deep learning-based RGB-thermal image denoising: review and applications. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-15916-7
Yan Yu and Boon Giin Lee and Matthew Pike and Qian Zhang and Wan-Young Chung. "Deep learning-based RGB-thermal image denoising: review and applications." Multimedia Tools and Applications, 2024. https://doi.org/10.1007/s11042-023-15916-7
Yan Yu, Boon Giin Lee, Matthew Pike, Qian Zhang, Wan-Young Chung. 2024. Deep learning-based RGB-thermal image denoising: review and applications. Multimedia Tools and Applications. doi:10.1007/s11042-023-15916-7
Yan Yu and Boon Giin Lee and Matthew Pike and Qian Zhang and Wan-Young Chung, "Deep learning-based RGB-thermal image denoising: review and applications," Multimedia Tools and Applications, 2024. doi: 10.1007/s11042-023-15916-7
@article{multimedia-2024,
title={Deep learning-based RGB-thermal image denoising: review and applications},
author={Yan Yu and Boon Giin Lee and Matthew Pike and Qian Zhang and Wan-Young Chung},
journal={Multimedia Tools and Applications},
year={2024},
doi={10.1007/s11042-023-15916-7}
}
Image denoising, Thermal imaging, Deep learning, Computer vision, Object detection
Abstract
Recently, vision-based detection (VD) technology has been well-developed, and its general-purpose object detection algorithms have been applied in various scenes. VD can be divided into two categories based on the type of modality: single-modal (single RGB or single thermal) and bimodal. Image denoising is typically the first stage of image processing in VD, where redundant information and noisy data are removed to produce clearer images for effective object detection. This study reviews deep learning-based image denoising for RGB and thermal images, investigating the denoising procedure, methodologies, and performances of algorithms tested with benchmark datasets. After introducing denoising models, the main results on public RGB and thermal datasets are presented and analyzed, and conclusions of objective comparison in practical effect are drawn. This review can serve as a reference for researchers in RGB–infrared denoising, image restoration, and related fields.