Single image dehazing through improved atmospheric light estimation
Single image dehazing through improved atmospheric light estimation
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DOI：
10.1007/s11042-015-2977-7
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发表时间：
2015-10
影响因子：
3.6
通讯作者：
Huimin Lu-;Yujie Li;Shota Nakashima;S. Serikawa
中科院分区：
文献类型：
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作者：
Huimin Lu-;Yujie Li;Shota Nakashima;S. Serikawa
户外视觉的图像对比度增强对于智能汽车辅助交通系统非常重要。在恶劣天气条件下捕获的视频帧通常具有能见度差的特点。大多数图像去雾算法考虑使用硬阈值假设或用户输入来估计大气光。然而，最亮的像素有时是车灯或路灯等物体，特别是对于智能汽车辅助交通系统。简单地使用硬阈值可能会导致错误的估计。在本文中，我们提出了一种单一优化的图像去雾方法，该方法可以有效地估计大气光并通过半全局自适应滤波器的估计来消除雾霾。增强后的图像具有噪声小、暗区曝光良好的特点。处理后图像的纹理和边缘也得到显着增强。
Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems. The video frames captured in poor weather conditions are often characterized by poor visibility. Most image dehazing algorithms consider to use a hard threshold assumptions or user input to estimate atmospheric light. However, the brightest pixels sometimes are objects such as car lights or streetlights, especially for smart car auxiliary transport systems. Simply using a hard threshold may cause a wrong estimation. In this paper, we propose a single optimized image dehazing method that estimates atmospheric light efficiently and removes haze through the estimation of a semi-globally adaptive filter. The enhanced images are characterized with little noise and good exposure in dark regions. The textures and edges of the processed images are also enhanced significantly.