增强历史航空照片:基于非参考度量和照片解释元素的新方法
Abdullah Harun Incekara1, Dursun Zafer Seker2
1Department of Geomatics Engineering, Tokat Gaziosmanpasa University, Taslicitlik, Tokat 60150, Türkiye.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了深度学习超分辨率 (SR) 历史空中照片的层次数据集结构. 这种方法通过分开培训类别和使用BRISQUE指标选择高质量的补丁来改善图像增强.
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:
- 基于深度学习的超分辨率 (SR) 是提高低分辨率图像质量的领先技术.
- 历史上的空中摄影由于降解和内容的变化,给SR带来了独特的挑战.
- 现有的SR模型经常在没有专业结构的情况下与各种图像数据集作斗争.
研究的目的:
- 为灰度尺度历史航空照片的SR提出和评估一个层次数据集结构.
- 通过使用基本的SR模型,评估这种结构化方法对SR模型性能的影响.
- 为了将SR结果与非参考图像质量指标相关联.
主要方法:
- 基于照片解释元素创建了一个层次化的数据集:裸露土地/森林 (初级),住宅 (二级) 和农田 (三级).
- 每个类别都被单独训练,使用一个基本的SR模型,避免同时训练所有不同的图像类型.
- 为每个类别生成了增强的图像,并通过根据盲人/无参考图像空间质量评估器 (BRISQUE) 评分选择高质量的5x5像素补丁来组装最终图像.
主要成果:
- 与传统培训方法相比,层次化的数据集结构导致了SR结果的改善.
- 图像类别的单独训练使基本的SR模型能够更有效地处理各种特征.
- 使用BRISQUE和基于参考的指标进行质量评估证实了结构化数据集方法的积极影响.
结论:
- 一个层次化的数据集结构显著提高了对历史空中照片的基本超分辨率模型的性能.
- 训练单独类别和选择高质量的补丁的方法提高了增强图像的保真度.
- 这种方法为改善历史图像档案中的图像质量提供了一个可行的策略.
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