现实世界超级分辨率与基于VLM的退化先前学习
Xiaxu Chen1,2,3, Duixu Mao1,2,3, Jun Ke4,5,6
1School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Scientific reports
|August 7, 2025
概括
本研究介绍了DePLSR,这是一种用于现实世界图像超分辨率 (Real-SR) 的新方法,它使用视觉语言模型来识别图像退化. DePLSR显著提高复杂退化的重建保真度,提高图像质量.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 现实世界图像超分辨率 (Real-SR) 被复杂的,未知的退化所挑战,这些退化妨碍了重建的准确性.
- 准确估计这些现实世界的退化对于SR模型来弥合合成训练数据和实际成像条件之间的差距至关重要,但这仍然是一个开放的问题.
研究的目的:
- 提出DePLSR,一种利用预训练的视觉语言模型的方法,用于在Real-SR.中有效地学习降解特征.
- 提高超分辨率图像的真实性和质量,特别是那些受到复杂和模两可的退化影响的图像.
主要方法:
- DePLSR使用降解适配器来预测低分辨率 (LR) 图像的降解特征,同时保留内容.
- 一个语义驱动的降解管道和一个带有标题的混合降解数据集是为培训而开发的.
- 引入了一个交叉模式的融合模块,将降解信息集成到下游SR模型中.
主要成果:
- 该DePLSR方法在峰值信号噪声比 (PSNR) 上取得了显著的0.98dB的改进.
- 实验证明了DePLSR在提取真实图像退化和增强合成和真实世界数据集上的超分辨率性能方面的卓越能力.
- 视觉化证实了严重降解的LR图像的改善恢复和复杂降解的有效去除.
结论:
- DePLSR有效地解决了Real-SR中未知的降解的挑战,通过多式联络方法学习降解特征.
- 拟议的方法显著提高了图像超分辨率性能,为复杂和退化的现实世界图像提供了更好的恢复.
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