Optimized contrast enhancement for real-time image and video dehazing
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【摘要】本文提出了一种针对含有雾的图像和视频快速、完善的去雾算法。观察发现有雾的图像普遍具有低对比度,我们通过增强对比度来修复图像。然后多度的增强这些低对比度会截断像素值以及导致信息丢失。因此,我们引入一个包含对比项以及信息丢失项的损失函数。通过最小化损失函数,该算法不仅增强了对比度而且有效的保留了图像信息。另外,我们将图片去雾算法扩展到视频去雾。我们通过计算透射率的相关性减少对视频去雾时的闪烁程度。实验证明该算法去雾的有效性以及快速地进行实时去雾。
Hazing modeling
解决问题之前,通常要对研究的问题建立数学模型。大气散射模型描述了雾化图像的退化过程,下述方程所描述的雾图形成模型被广泛使用。
其中,
Fig.1 Block diagram of the proposed static image dehazing algorithm.
Static image dehazing
Atmospheric light estimation
与其它大气光
- 我们将图片分为四个矩形区域。
- 我们将每个区域的平均像素值减去该区域的标准差,得到该区域的得分score。
- 将score最高的区域划分为四个小区域。
- 重复2~3步骤,直到最高score区域的size小于预先设定的阈值。
- 取该区域的最亮点。
如图所示。
Fig. 2. Atmospheric light estimation. By recursively dividing an image into four smaller regions and selecting the region with the highest score, we determine the region that is hazed most densely and then choose the atmospheric light within the region. In this example, the red block is the selected region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Optimal transmission estimation
我们给出了三个恢复块对比的定义。它们分别为:
- Mean squared error (MSE) contrast
- Michelson contrast
- Weber contrast
Fig. 3. 比较三个对比定义在去雾的效果。(a)输入图像.去雾图像和与其对应的转换映射表 (b) the MSE contrast, (c) the Michelson contrast, (d) the Weber contrast. 在转换映射表中,黄色和红色像素分别表示近、远景点。(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
我们注意到fig.3的三个去雾结果非常接近。所以在后续的去雾工作中,采用MSE contrast。当然其它两个和MSE contrast一样有效。
上式可知,
Fig. 4. An example of the transformation function. Input pixel values are mapped to output pixel values according to the transformation function, depicted by the black line. The red regions represent the information loss due to the truncation of output pixel values. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 5. Relationship between the transmission value and the information loss. A smaller transmission value causes more severe truncation of pixel values and a larger amount of information loss. (a) An input hazy image. The restored dehazed images with transmission values of (b)
为了解决在增强对比度的同时尽可能保留原图像信息。我们定义了两个损失函数,分别为对比度,信息丢失函数:
上式中,
Transmission refinement
这部分有迷糊。我讲讲我的理解。和何凯明的基于暗通道去雾的透射率
之前我们针对整幅图提出了透射率
窗口每间隔一个像素滑动整幅图像,这就就会有多个窗口重叠在每个像素点。在每个像素点,我们取该点所有相关透射率的平均值作为该点的最终透射率。我们称这种方法为中心窗口方案(方法)。但是这个方法带来的问题会对图像边缘进行平滑。为了解决这个问题,提出了可移动的窗口来代替中心窗口。
Fig. 6. Illustration of the shiftable window scheme: (a) centered window and (b) shiftable window
After obtaining the pixel-based transmission map, we dehaze the input image based on (1). However, as suggested in [11], we constrain the minimum transmission value to be greater than 0.1, since a smaller value tends to amplify noise. Furthermore, the restored hazy image often has darker pixel values than the input image. Thus, we apply the gamma correction [1] to the restored image with an empirically selected gamma of 0.8.
Fig. 7. Transmission map refinement: (a) Input hazy images, (b) the block-based transmission maps, and the pixel-based transmission maps using (c) the centered window scheme and (d) the shiftable window scheme. In the transmission maps, yellow and red colors represent near and far scene points, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Video dehazing
Temporal coherence
上面怎么翻译,叫“临时的一致性”?姑且就这么叫吧。
对于视频去雾,我们需要将原视频变换到YUV,然后仅对Y分量进行计算,以减少算法复杂度。我们假设帧于帧之间的原图像具有如下性质:
Y为通道,J为原图像,P为像素点,K为帧。同时假设大气光A在整个视频中的值不变,当然有可以重新计算。我们也能够轻易获取透射率之间的关系。
但问题在于我比较两个像素点是在K、K-1帧相同的位置,而物体又是移动的以及相同的景深指向不同的像素点,为了解决这个问题,我们需要对像素点进行跟踪。但跟踪算法太费时了,所以我们又引入了一个概率模型。
Fast transmission refinement
we use a Gaussian window pixels around the window center have higher weights, whereas pixels farther from the center have lower weights. Then, we obtain the final optimal transmission value for each pixel, by computing the Gaussian weighted sum of the transmission values associated with the overlapping windows.
尾巴
鲁棒性不错的去雾算法,较何凯明的基于暗通道的去雾算法在对天空处理上的效果要好。
论文及原作者的代码下载地址:http://mcl.korea.ac.kr/projects/dehazing/#userconsent#
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