Liu Dingshuo, Zhou Jingchun, Xie Xiong, Lin Zifan, Lin Yi
Opt Express. 2022 Aug 1;30(16):29099-29116. doi: 10.1364/OE.462861.
In underwater images, the significant sources of distortion are light attenuation and scattering. Existing underwater image restoration technologies cannot deal with the poor contrast and color distortion bias of underwater images. This work provides a new underwater image restoration approach relying on depth map optimization and background light (BL) estimation. First, we build a robust BL estimation model that relies on the prior features of blurriness, smoothness, and the difference between the intensity of the red and blue-green channels. Second, the red-light intensity, difference between light and dark channels, and disparity of red and green-blue channels by considering the hue are used to calculate the depth map. Then, the effect of artificial light sources on the underwater image is removed using the adjusted reversed saturation map. Both the subjective and objective experimental results reveal that the images produced by the proposed technology provide more remarkable visibility and superior color fidelity.
在水下图像中，显著的失真源是光衰减和散射。现有的水下图像恢复技术无法处理水下图像对比度差和颜色失真偏差的问题。这项工作提供了一种新的水下图像恢复方法，该方法依赖于深度图优化和背景光（BL）估计。首先，我们构建了一个强大的BL估计模型，该模型依赖于模糊度、平滑度以及红通道与蓝绿通道强度差异的先验特征。其次，通过考虑色调，利用红光强度、明暗通道差异以及红通道与绿蓝通道的视差来计算深度图。然后，使用调整后的反向饱和度图消除人造光源对水下图像的影响。主观和客观实验结果均表明，所提技术生成的图像具有更显著的可见性和更高的颜色保真度。
