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MDFN:通过多维扭曲功能增强电网图像质量评估

Zhenyu Chen¹, Jianguang Du¹, Jiwei Li¹

¹Big Data Center, State Grid Corporation of China, Beijing 100031, China.

Sensors (Basel, Switzerland)

|September 19, 2025

概括

准确的电网图像质量评估对于深度学习至关重要. 一个新的多维扭曲特征网络 (MDFN) 有效地分析图像特征,包括噪音和亮度,改善质量选.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
电气工程电气工程

背景情况:

低质量的电网图像会降低电力行业的深度学习性能.
当前的盲人图像质量评估 (BIQA) 方法通常依赖于单一的特征,忽略了噪音和亮度等关键因素.
电网图像经常受到噪音,低曝光和过度曝光的影响,需要专门的质量评估.

研究的目的:

开发用于电网图像的先进图像质量评估技术.
通过结合多维特征来解决现有的BIQA方法的局限性.
为深度学习应用提高选高质量的电网图像的准确性.

主要方法:

提出了一个多维扭曲特征网络 (MDFN),集成卷积神经网络 (CNN) 和变压器架构.
采用双分支特征提取器:CNN用于本地特征,变压器用于本地和全球特征.
引入了一个频率选择模块 (FSM) 来分离和融合高频 (边缘,细节) 和低频 (语义,结构) 组件.
开发了一种用于提取和集成噪声和亮度特征的新方法,用CLS代币进行质量预测.

主要成果:

与现有方法相比,拟议的MDFN方法表现出优异的性能.
在三个公共数据集和一个专门的电网图像数据集中验证了实验结果.
该网络有效地捕捉了本地和全球图像特征,以及噪音和亮度扭曲.

结论:

在电力行业中,MDFN为盲人图像质量评估提供了更准确,更强大的解决方案.
整合多维特征,包括频率组件,噪声和亮度,显著提高了评估准确性.
该方法在选高质量的电网图像方面是有效的,这对于可靠的深度学习模型培训和性能至关重要.

关键词:

亮度亮度亮度亮度选择频率的频率选择.图像质量评估图像质量评估多维扭曲特征具有多维扭曲特征.噪音噪音噪音噪音电力电网的电力系统.

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