@article{SHI2025, 
author = {Qingaoxue SHI and Chaoran YANG and Xinda LIU and Guohua GENG},
title = {Qin Opera video denoising algorithm based on attention mechanism},
year = {2025},
journal = {Journal of Northwest University (Natural Science Edition)},
volume = {55},
number = {1},
pages = {168-179},
keywords = {Qin Opera, video denoising, attention mechanism, temporal fusion},
url = {https://www.sciopen.com/article/10.16152/j.cnki.xdxbzr.2025-01-014},
doi = {10.16152/j.cnki.xdxbzr.2025-01-014},
abstract = {Qin Opera, as a treasure of Chinese traditional theatre art, has a profound historical heritage. However, the early video materials of Qin Opera are often affected by noise and distortion, resulting in poor picture quality, which seriously hampers the preservation quality of Qin Opera digital archives. Currently applied video denoising techniques often do not make full use of the temporal coherence of the video frame sequence when dealing with the colorful and complex texture of Qin Opera’s costumes, which makes the denoising effect unsatisfactory and makes it difficult to effectively retain the core features of the video frames. In this paper, we carry out research on the Qin Opera video denoising algorithm based on the attention mechanism, and the main research contents are as follows: Aiming at the existing video denoising algorithms ignoring the temporal correlation between frames which leads to the problem of poor effect, we propose a new video denoising algorithm, which makes use of the double gating attention mechanism for the fusion of the temporal sequence information. The algorithm firstly integrates the timing information of consecutive video frames effectively through the timing fusion module; then accurately identifies and eliminates the timing noise using the dual-gated attention denoising network; finally, the features are further refined through the multi-head interactive attention refining module to eliminate the artifacts that may be generated during the denoising process and recover the lost details, to enhance the quality of the denoised image. The experimental results demonstrate that compared with existing methods such as DVDNet, ViDeNN, and FastDVDNet, this method can make better use of the timing information of the video to achieve clean and efficient denoising of Qin Opera videos.}
}