School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2023 Jun 19;23(12):5708. doi: 10.3390/s23125708.
Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel's transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G-B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.
水下图像由于水介质的光吸收和散射，往往会出现严重的质量下降，如能见度差、对比度降低和颜色偏差。对于这些图像来说，提高能见度、改善对比度和消除颜色失真都是极具挑战性的问题。本文提出了一种基于暗通道先验（DCP）的水下图像和视频的有效高速增强和恢复方法。首先，提出了一种改进的背景光（BL）估计方法，以准确估计 BL。其次，基于 DCP 粗略估计 R 通道的传输图（TM），并设计了一个集成场景深度图和自适应饱和度图（ASM）的 TM 优化器来细化上述粗略 TM。然后，通过 G-B 通道与红通道衰减系数的比值计算 G-B 通道的 TM。最后，采用改进的颜色校正算法来提高可见度和亮度。采用几种典型的图像质量评估指标来验证，与其他先进方法相比，该方法能更有效地恢复水下低质量图像。还在鳍推进水下机器人-机械手系统上进行了水下视频实时测量，以验证该方法在真实场景中的有效性。
