将单图像超分辨率模型调整为视频超分辨率:一个插即用方法
Wenhao Wang1, Zhenbing Liu1, Haoxiang Lu1
1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究提出了一种具有成本效益的方法,用于将单图像超分辨率 (SISR) 模型适应视频超分辨率 (VSR) 任务. 该方法通过集成时间特征提取模块来提高视频质量,优于现有的VSR模型.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 视频质量受到传感器能力的限制,需要视频超分辨率 (VSR) 技术.
- 开发专门的VSR模型在计算上昂贵,资源密集.
研究的目的:
- 为VSR任务提出一种新且具有成本效益的方法,以适应现有的单图像超分辨率 (SISR) 模型.
- 提高视频增强SISR模型的性能,而不需要全新的架构.
主要方法:
- 进行了SISR模型调整的正式分析.
- 开发了一个plug-and-play时间特征提取模块,包括偏移估计,空间聚合和时间聚合子模块.
- 该模块将多个的特征对齐并融合,然后将它们输入SISR模型进行重建.
主要成果:
- 使用拟议的方法,成功调整了5个代表性的SISR模型.
- 调整后的模型显示了Vid4基准值的显著改善,PSNR增加至少1.26dB,SSIM增加0.067.
- 适应VSR的模型的性能超过了当前最先进的VSR方法.
结论:
- 拟议的适应方法在各种SISR模型中有效提高视频超分辨率.
- 这种方法为提高视频质量提供了实用和有效的解决方案,降低了与VSR模型开发相关的成本.
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