基于改进颜色线模型的水下图像增强算法

Underwater image enhancement algorithm based on improved color-line model

为解决水下设备采集图像存在的雾化和颜色失真等退化问题,提出一种基于改进颜色线模型的水下图像增强方法。首先,提出一种基于改进四叉树细分的水下背景光估计方法,消除水下因素的干扰,得到更加准确的背景光估计值。其次,建立基于颜色线规律的局部透射率优化模型,并设计一种新的Gauss-Seidel型交替线性极小化(Gauss-Seidel Type Inertial Proximal Alternating Linearized Minimization Algorithm,GiPALM)非凸优化方法求解透射率,在提高模型收敛速度的同时得到更加准确的透射率估计值。最后,在背景光和透射率估计得到恢复图像的基础上,进一步采用线性拉伸校正图像的颜色信息,得到符合人眼感官视觉的水下增强图片。实验结果表明,本文方法在主观评价、客观评价、颜色准确度和应用测试等方面均优于其他先进算法,展现了出色的性能,有效提高了水下图像的清晰度和可视性。

To solve the degradation problems such as hazing and color distortion of images captured by underwater devices,an underwater image enhancement method based on improved color-line model is proposed.Firstly,an underwater background light estimation method based on improved quadtree subdivision is proposed to eliminate the interference of underwater factors and obtain more accurate background light estimates.Then,a local transmittance optimization model based on the color-line law is established,and a new Gauss-Seidel type inertial proximal alternating linearized minimization algorithm(GiPALM)nonconvex optimization method is designed to solve the transmittance,which improves the convergence speed of the model and yields a more accurate transmittance estimation at the same time.Finally,based on the background light and transmittance estimation to obtain the recovered image,linear stretching is further used to correct the color information of the image to obtain an underwater enhanced picture that conforms to the sensory vision of the human eye.The experimental results show that our method is superior to other comparative algorithms in terms of qualitative evaluation,quantitative evaluation,color accuracy and application testing.Our method shows excellent performance and effectively improves the clarity and visibility of underwater images.