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    这项研究引入了一种改进的图像退化模型,用于单图像退化. 新的等级解卷策略提高了图像恢复的准确性,在雾条件下显著提高了视觉质量.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 图像处理 图像处理

    背景情况:

    • 雾通过光散射和气溶和水滴的吸收显著降低了图像质量.
    • 退化表现为亮度和对比度降低,模糊度增加和噪音.
    • 有效的图像退化模型对于单个图像的退化至关重要.

    研究的目的:

    • 提出一个有效的图像退化模型,用于单个图像的退化.
    • 为了提高图像恢复的准确性,使用层次的解卷策略.
    • 为了提高在雾条件下拍摄的图像质量.

    主要方法:

    • 一个新的图像退化模型,基于传输地图细分的层次解卷.
    • 使用暗通道前 (DCP) 方法估计传输地图.
    • 图像区域细分基于传输直方图的拟合和特定区域逆卷积水平的应用.
    • 来自不同区域的子图像的融合,以生成无化图像.

    主要成果:

    • 拟议的方法在合成和天然雾图像上进行了测试.
    • 使用PSNR,SSIM,图像,NIQE和BRISQUE进行定量评估.
    • 主观和客观评估表明,与八种先进的除尘方法相比,结果具有竞争力.

    结论:

    • 提出的等级解卷策略有效地提高了单图像解卷中的图像恢复精度.
    • 该方法在恢复被雾损坏的图像方面取得了竞争力的表现.
    • 这种方法为在恶劣天气条件下提高可见度提供了有希望的解决方案.