The presence of haze degrades the quality of a captured image. The aerosols in the atmosphere cause scattering of the incident light, and this phenomenon is observed on the captured image as well, where some regions appear grayish and colors in those regions appear faded. To improve the quality of the hazy images, we propose a joint fusion and restoration model that sufficiently enhances the contrast of the hazy image while preserving its mean brightness. The fusion model utilizes images of various exposures generated by a modified Gamma correction model. The images for fusion are selected using some selection criteria and fused in a multi-resolution decomposition scheme. Three haze-sensitive weight maps corresponding to some statistical property of haze namely, saliency, illumination, and luminance gradient are constructed. The hazy image formation model is then used and dehazing is performed based on the dark channel prior assumption. The proposed algorithm does not consider the estimation of airlight vector which seldom cause over-saturation defect, instead a mean brightness preservation model has been applied. The variety of experiments demonstrate the significance of the proposed method.

中文翻译：

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用于增强雾度退化图像的联合 Gamma 校正和多分辨率融合方案

雾霾的存在会降低所捕获图像的质量。大气中的气溶胶会引起入射光的散射，在捕获的图像上也可以观察到这种现象，其中一些区域呈灰色，而这些区域的颜色呈褪色状态。为了提高朦胧图像的质量，我们提出了一种联合融合和恢复模型，该模型在保持其平均亮度的同时充分增强了朦胧图像的对比度。融合模型利用由修改后的 Gamma 校正模型生成的各种曝光的图像。使用一些选择标准来选择用于融合的图像，并在多分辨率分解方案中进行融合。构建了三个对应于雾的一些统计特性的雾敏感权重图，即显着性、光照度和亮度梯度。然后使用模糊图像形成模型并基于暗通道先验假设执行去雾。所提出的算法没有考虑很少引起过饱和缺陷的空气光矢量的估计，而是应用了平均亮度保持模型。各种实验证明了所提出方法的重要性。