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导入扩散和重新设计的逆向过程用于图像去雨

Jhe-Wei Lin¹, Cheng-Hsuan Lee¹, Tang-Wei Su¹

¹Department of Information Engineering and Computer Science, Feng Chia University, Taichung City 407, Taiwan.

Sensors (Basel, Switzerland)

|June 27, 2024

概括

这项研究引入了一种新的扩散模型,用于图像降雨,增强特征保留和现实主义. DIR-SDE方法通过有效地消除噪音,同时保持细节,从而提高图像质量.

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:

高质量的图像在各个领域都是必不可少的.
消除噪音对于提高图像质量至关重要.
扩散模型是图像恢复的一个有希望的方法.

研究的目的:

提出一种新的扩散模型 (DIR-SDE) 进行图像降雨.
在降雨过程中增强功能保留和图像现实性.
为了提高整体的图像恢复性能.

主要方法:

建议使用DIR-SDE方法,参考IR-SDE和IDM扩散模型.
使用IR-SDE作为基础结构,并通过整合DINO-ViT进行了改进.
使用DINO-ViT提取图像特征,并在扩散过程中与原始图像融合.

主要成果:

在Rain100H数据集上,DIR-SDE方法显示了更好的性能.
与IR-SDE相比,DIR-SDE在SSIM和LPIPS中实现了0.003的增长.
DIR-SDE导致FID下降了1.23,这表明了更好的现实主义.

结论:

拟议的DIR-SDE扩散模型有效地增强了图像恢复.
整合DINO-ViT可以改善功能提取和学习.
该方法在图像清除任务中显示出显著的改进.

关键词:

没有下雨的干净了.扩散模型的扩散模型.功能提取特性提取图像处理过程图像处理过程

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