Abstract. Images collected in fog can lose contrast and fidelity due to the scattering and absorption of light by the fog particles. In particular, unlike common white fog, colored fog exhibits varying degrees of light attenuation at different wavelengths, resulting in the simultaneous deviation of contrast and color. We present the failure of the traditional algorithm in the colored haze removal process. In order to eliminate the negative influence of colored fog on images, we introduce a hazy image restoration algorithm based on the modified color balance and variant of the dark-channel dehazing process. Our proposed technique corrects the color deviation, such that the transmission of each color channel is consistent, thus allowing for defogging via the dark-channel principle to improve the image quality. Moreover, we use the L0 gradient minimization algorithm to improve the dark-channel algorithm, thus optimizing the output of underwater image processing. Our proposed method is implemented for the restoration of several hazy underwater images, proving its ability to effectively eliminate the influence of different types of colored fog, overcoming the key limitation of the traditional dehazing approach.

中文翻译：

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用于去除彩色雾霾的色彩平衡和去雾算法

摘要。由于雾粒子对光的散射和吸收，在雾中收集的图像可能会失去对比度和保真度。特别是与普通的白雾不同，彩色雾在不同波长处表现出不同程度的光衰减，导致对比度和颜色同时出现偏差。我们介绍了传统算法在有色雾度去除过程中的失败。为了消除彩色雾对图像的负面影响，我们引入了一种基于改进的颜色平衡和暗通道去雾过程变体的模糊图像恢复算法。我们提出的技术校正了颜色偏差，使每个颜色通道的传输一致，从而允许通过暗通道原理进行去雾以提高图像质量。而且，我们使用L0梯度最小化算法来改进暗通道算法，从而优化水下图像处理的输出。我们提出的方法用于恢复多幅朦胧的水下图像，证明其能够有效消除不同类型有色雾的影响，克服了传统去雾方法的关键局限性。