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Correction of Simple Contrast Loss in Color Images

机译:彩色图像中简单对比度损失的校正

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This paper is concerned with the mitigation of simple contrast loss due to added lightness in an image. This added lightness has been referred to as "airlight" in the literature since it is often caused by optical scattering due to fog or mist. A statistical model for scene content is formulated that gives a way of detecting the presence of airlight in an arbitrary image. An algorithm is described for estimating the level of this airlight given the assumption that it is constant throughout the image. This algorithm is based on finding the minimum of a global cost function and is applicable to both monochrome and color images. The method is robust and insensitive to scaling. Once an estimate of airlight is achieved, then image correction is straightforward. The performance of the algorithm is explored using the Monte Carlo simulation with synthetic images under different statistical assumptions. Several examples of before and after color images are given. Results with real video data obtained in poor visibility conditions indicate frame-to-frame consistency of better than 1% of maximum level
机译:本文关注的是减轻由于图像中增加的亮度而导致的简单对比度损失。这种增加的亮度在文献中被称为“空中照明”,因为它通常是由于雾或薄雾引起的光学散射而引起的。建立了场景内容的统计模型,该模型提供了一种检测任意图像中是否存在空中照明的方法。描述了一种算法,假定在整个图像中它是恒定的,则该算法用于估计该光照度。该算法基于找到全局成本函数的最小值,并且适用于单色和彩色图像。该方法是鲁棒的并且对缩放不敏感。一旦获得了对空中照明的估计,那么图像校正就很简单了。在不同的统计假设下,使用带有合成图像的蒙特卡洛模拟来探索算法的性能。给出了彩色图像之前和之后的几个示例。在可见度不佳的情况下获得的真实视频数据结果表明,帧间一致性优于最大级别的1%

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