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秦腔，作为中国传统戏曲艺术的瑰宝，拥有深厚的历史底蕴。然而，秦腔早期的影像资料常受噪声和失真影响，导致画质不佳，严重妨碍了秦腔数字档案的保存品质。目前应用的视频去噪技术在处理秦腔那色彩丰富、纹理复杂的服饰时，往往没有充分利用视频帧序列的时间连贯性，使得去噪效果并不理想，难以有效保留视频帧的核心特征。基于注意力机制的秦腔视频去噪算法开展研究，针对现有视频去噪算法忽略帧间时序相关性导致效果不佳的问题，提出了一种新的视频去噪算法，该算法利用双门控注意力机制进行时序信息的融合。首先，通过时序融合模块，将视频连续帧的时序信息进行有效整合；其次，利用双门控注意力去噪网络精确识别并消除时序上的噪声；最后，通过多头交互注意力精炼模块进一步细化特征，以消除去噪过程中可能产生的伪影并恢复丢失的细节，从而提升去噪后图像的质量。实验结果表明，与DVDNet、ViDeNN以及FastDVDNet等现有方法相比，该方法可以更好地利用视频的时序信息，达到干净且高效的秦腔视频去噪效果。
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.
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基本信息:
DOI：10.16152/j.cnki.xdxbzr.2025-01-014
中图分类号:J825;TP391.41
引用信息:
[1]师秦高雪,杨超然,刘鑫达等.注意力机制的秦腔视频去噪算法[J].西北大学学报(自然科学版),2025,55(01):168-179.DOI:10.16152/j.cnki.xdxbzr.2025-01-014.
基金信息:
国家自然科学基金(62271393); 文化和旅游部重点实验室项目(1222000812,cr2021K01); 西安市社会发展科技创新示范项目(2024JH-CXSF-0014)