基于光值加权分配和多层受限感知的图像消光算法
Dongyang Shi1, Sheng Huang2,3, Wei Zhao1
1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Scientific reports
|April 11, 2025
概括
通过改进明亮区域处理和抗噪声,DWARP算法增强了图像除雾功能. 这种新的方法在雾条件下实现了卓越的性能,有利于智能运输等应用.
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:
- 现有的图像消除模型在明亮的区域中扎着扭曲,缺乏对噪声的强度.
- 有效的排气对于智能运输等应用至关重要.
研究的目的:
- 提出一种新的图像除算法,DWARP (Dehazing With light-value weighted Allocation and multi-layer Restricted Perception),该算法解决了明亮区域处理和噪声强度的局限性. 提出一个新的图像除算法,DWARP (Dehazing With light-value weighted Allocation and multi-layer Restricted Perception),解决了明亮区域处理和噪声强度的局限性.
- 为了提高图像处理的准确性和效率.
主要方法:
- 开发了一个大气光估计模块,使用加权分配与三阶段的精炼过程.
- 实施了多层受限感知方案用于传输率估计,将明亮区域的扭曲转化为传输率误差减少.
- 集成了一个亮度调节模块和高斯式无光模块,以提高视觉质量和抗噪.
主要成果:
- 该DWARP算法有效地防止了明亮区域的扭曲,并显著提高了噪声强度.
- 在四个数据集中实现了高平均性能指标:PSNR (37.41 dB),SSIM (88.74%) 和VIF (0.89).
- 在PSNR,SSIM和VIF中超越了RIDCP算法,同时也提高了除效率.
结论:
- 在图像 dehazing 的改进中,DWARP 算法证明了科学和理论上的正确性.
- 该模型有效地处理像天空这样的明亮区域,并表现出强大的抗噪能力.
- 介绍了一种新有效的雾清除方法,推进了智能运输等领域.
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