一个具有黑暗道优先级的多层次脱气网络.
Guoliang Yang1, Hao Yang1, Shuaiying Yu1
1School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种具有暗通道先验 (MSDN-DCP) 的新型多尺度除网络,以改进图像除. 在MSDN-DCP有效地解决问题,如不完整的dehazing和颜色偏差,提高视觉质量.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 卷积神经网络 (CNN) 在图像处理方面表现有前途.
- 现有的基于CNN的方法在不完整的脱,颜色偏差和细节损失方面扎.
研究的目的:
- 提出一个新的多尺度除尘网络与黑暗通道先验 (MSDN-DCP).
- 为了增强特征提取,融合和精细化,以实现卓越的图像脱.
主要方法:
- 引入了一个具有双分支残余结构的特征提取模块 (FEM).
- 设计了一个特征融合模块 (FFM),用于自适应的多尺度特征组合.
- 提出了一个使用暗通道先验理论的暗通道精炼模块 (DCRM).
主要成果:
- 在Haze4K数据集上达到29.57dB的峰值信号噪声比 (PSNR).
- 在Haze4K数据集上达到98.1%的结构相似性 (SSIM).
- 在客观指标和视觉感知上,在现有算法上表现出优越的除尘性能.
结论:
- 拟议的MSDN-DCP有效地克服了以前基于CNN的除烟方法的局限性.
- 该网络在图像消毒方面取得了最先进的结果.
- MSDN-DCP提供了改善的视觉质量和细节保存在dehazed图像.
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