多分辨率学习和语义边缘增强用于城市场景图像的超分辨率语义细分
Ruijun Shu1,2, Shengjie Zhao1,3
1College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究引入了超分辨率语义细分 (SRSS) 的新框架,可以增强特征提取和边缘检测. 提出的方法在具有挑战性的基准指标上实现了最先进的性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像处理 图像处理
背景情况:
- 超分辨率语义细分 (SRSS) 旨在从低分辨率输入中生成高分辨率细分,从而降低资源有限的设备的计算成本.
- 现有的SRSS方法通常需要网络修改或复杂的模块,限制部署灵活性,并因信息丢失而难以实现语义边缘准确性.
研究的目的:
- 提出一个简单而有效的框架,MS-SRSS,用于超分辨率的语义细分.
- 为了增强特征提取能力,并改进语义边缘检测在SRSS.
主要方法:
- 引入了多分辨率学习机制 (MRL),以提高特征编码器的提取能力.
- 开发了一个语义边缘增强损失 (SEE) 以减轻语义边界的错误检测.
- 该框架旨在与现有的基于编码器-解码器的语义细分网络兼容.
主要成果:
- 在Cityscapes,Pascal Context和Pascal VOC 2012基准上进行了广泛的实验.
- 与现有的SRSS技术相比,拟议的MS-SRSS方法表现出优越的性能.
- 在超分辨率语义细分方面取得了新的最先进的结果.
结论:
- MS-SRSS框架为超分辨率语义细分提供了灵活有效的解决方案.
- 多分辨率学习和语义边缘增强的结合显著提高了细分精度,特别是在对象边界.
- 该方法为在资源有限的设备上高效,高分辨率的语义细分提供了实用方法.
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