Sun Chunsheng, Ding Zhichao, Ma Liheng
College of Ordnance Engineering, Naval University of Engineering, Wuhan 430033, China.
Heliyon. 2023 Apr 28;9(5):e15849. doi: 10.1016/j.heliyon.2023.e15849. eCollection 2023 May.
Image dehazing is desired under the foggy, rainy weather, or the underwater condition. Since the polarization-based image dehazing utilizes additional polarization information of light to de-scatter, image detail can be recovered well, but how to segment the polarization information of the background radiance and the object radiance becomes the key problem. For solving this problem, a method which combing polarization and contrast enhancement is demonstrated. This method contains two main steps, (a) by seeking the region of large mean intensity, low contrast and large mean degree of polarization, the no-object region can be found, and (b) through defining a weight function and judging whether the dehazed image can achieve high contrast and low information loss, the degree of polarization for object radiance can be estimated. Based on the estimated parameters, the scatter of light by the mediums can be diminished considerably. The theoretical derivation shows that this method can achieve advantages complementation, such as being able to obtain more details like the polarization-based method and high image contrast like the contrast enhancement based method. Besides, it is physically sound and can achieve good dehazing performance under different conditions, which has been verified by different hazing polarization images.
在雾天、雨天或水下环境下，图像去雾是很有必要的。由于基于偏振的图像去雾利用了光的额外偏振信息来消除散射，图像细节能够得到很好的恢复，但如何分割背景辐射和物体辐射的偏振信息成为了关键问题。为了解决这个问题，展示了一种结合偏振和对比度增强的方法。该方法包含两个主要步骤：(a) 通过寻找平均强度大、对比度低且平均偏振度大的区域，可以找到无物体区域；(b) 通过定义一个权重函数并判断去雾后的图像是否能实现高对比度和低信息损失，来估计物体辐射的偏振度。基于估计的参数，可以显著减少介质对光的散射。理论推导表明，该方法能够实现优势互补，比如能够像基于偏振的方法一样获得更多细节，像基于对比度增强的方法一样具有高图像对比度。此外，它在物理上是合理的，并且在不同条件下都能实现良好的去雾性能，这已经通过不同的有雾偏振图像得到了验证。
