多分支网络用于使用扩展卷积和注意力机制的彩色图像剥离
Minh-Thien Duong1, Bao-Tran Nguyen Thi1, Seongsoo Lee2
1Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的多分支网络,用于先进的图像消除,增强复杂图像的美学恢复. 拟议的方法在客观和主观评估中明显优于现有的深度学习技术.
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:
- 图像剥离是计算机视觉中一个具有挑战性的不良问题.
- 基于卷积神经网络 (CNN) 的方法显示出有前途,但在复杂的图像内容方面存在困难.
- 简单的网络往往无法恢复美观的图像.
研究的目的:
- 通过使用多分支网络提出改进的图像染方法.
- 为了提高从噪音输入中恢复美观图像的效果.
- 解决简单的无线化网络在处理复杂图像内容方面的局限性.
主要方法:
- 建议基于自动编码器架构的多分支网络.
- 嵌入了金字塔上下文模块 (PCM),使用扩展卷积来扩大受体场.
- 结合了剩余瓶注意模块 (RBAM) 来改进功能并减少工件.
主要成果:
- 拟议的网络有效地学习了多层次的上下文特征.
- PCM成功地解决了全球信息丢失的问题.
- RBAM消除了退化的功能,并最大限度地减少了不必要的工件.
- 广泛的实验表明性能优于最先进的方法.
结论:
- 拟议的多分支网络显著改善了图像破坏性能.
- 集成PCM和RBAM模块增强了特征提取和文物减少.
- 与现有的深度学习方法相比,该方法实现了优越的客观和主观结果.
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