减少色彩失真的自适应单幅图像去雾算法

基于暗通道先验的单幅图像去雾算法是目前最为高效的图像去雾技术，然而当图像中某些场景不完全满足暗通道先验时，去雾处理后的图像中常常出现大量伪影和色彩失真，因此需要根据图像对该方法进行修正。假设图像场景亮度越高，暗通道越不可信；场景饱和度越低，暗通道越不可信。基于该假设重新设计了图像的暗通道置信度，以补偿场景不完全满足暗通道先验时估算过大的暗通道值；另外对图像进行后置增强处理，以提升图像的视觉效果。对三类具有代表性的雾天图像进行实验，实验结果显示，与相关算法相比，该算法在缓解色彩失真、去除伪影等方面表现更好。该算法通过设计暗通道置信度克服了图像场景不完全满足暗通道先验时暗通道值估计偏大的问题，显著提升了暗通道先验去雾模型对不同雾天场景的适应能力。

Color preserved self-adaptive single image defogging method

The single image haze removal algorithm based on dark channel priori is currently the most efficient image defogging technology. However, when the image does not fully satisfy the dark channel priori, many artifacts and color distortion will appear in the processed image, so that the method should be modified according to the image situation. This paper proposed the hypothesis that if the scene was brighter, the less credible the dark channel priori would be; if the scene saturation was lower, the less credible the dark channel priori would be. Based on this assumption, this paper designed an image''s dark channel confidence to limit the dark channel value when the scene didn''t fully satisfy the dark channel prior. Furthermore, the image was post-enhanced to improve the visual effect. Three typical fog weather images were selected to verify the effectiveness of the proposed method. The experimental results show that the proposed algorithm performs better in preserving color and removing artifacts comparing to some current related defogging algorithms. A new dark channel confidence calculation method is designed to overcome the problem that the estimation of dark channel value will be too large when the image scene does not fully satisfy the dark channel priori. The proposed method improves the adaptability of the dark channel priori defogging model to different foggy scenes.