应用于水下机器人的快速深海图像复原算法

Rapid Deep-Sea Image Restoration Algorithm Applied to Unmanned Underwater Vehicles

由于海水吸收和水中悬浮颗粒散射,水下机器人通过人造光源获得的深海图像普遍存在模糊、色偏和清晰度低等问题。围绕深海图像快速精准复原所需解决的关键问题,首先建立真实深海图像数据集,分析深海图像的成像特点,基于图像特征的统计结果提出一种线性景深模型,然后通过有监督方法进行模型参数辨识,最后根据景深模型分别快速估计出原始图像的传输地图和背景光,进而有效避免累计误差,实现深海图像的有效复原。实验结果表明,所提算法在图像复原结果、有效性、质量和实时性指标上均优于同类算法,在Nvidia Jetson TX2嵌入式设备上处理600 pixel×800 pixel大小的图像,平均复原速度是4种优秀水下图像增强算法中最快的3.08倍。

Due to the absorption of seawater and the scattering of suspended particles in water,the deep-sea image obtained by underwater robot through artificial light source is generally fuzzy,color deviation,and low resolution.Focusing on the key problems to be solved in the rapid and accurate restoration of deep-sea images,the data set of real deep-sea images is firstly established,and the imaging characteristics of deep-sea images are analyzed.Based on the statistical results of image features,a linear depth of field model is proposed.Then,the model parameters are identified by supervised method.Finally,according to the depth of field model,the transmission map and background light of the original image are estimated quickly,so as to effectively avoid cumulative error and achieve effective restoration of deep-sea images.Experimental results show that the proposed algorithm is superior to other algorithms in terms of image restoration results,validity,quality,and real-time performance.Processing 600 pixel×800 pixel image on Nvidia Jetson TX2 embedded device,the average restoration speed of the proposed algorithm is 3.08 times faster than the four outstanding underwater image enhancement algorithms.