基于暗原色先验模型的水下彩色图像增强算法

针对在水下环境中，光的散射和衰减导致水下光学成像质量严重下降，图像对比度低、颜色失真的问题，提出了一种暗原色先验和基于通道直方图量化的颜色校正算法相结合的图像增强新方法。对于待增强的水下彩色图像，首先建立水下光学图像成像模型，并利用优化与改进的暗原色先验算法对图像进行去模糊，然后通过分析R、G、B三通道的累积直方图，对去模糊后的彩色图像各通道灰度值进行量化，实现图像的颜色校正。实验结果表明，提出的方法可以有效地消除了由于光的散射造成图像的模糊，有效提高了水下图像的视觉效果，恢复水下图像的颜色平衡。

Underwater color image enhancement algorithm based on prior dark-channel model

Considering the problems of low contrast and color distortion caused by scattering and attenuation of the light in the underwater environment, the underwater color image enhancement method is presented in this paper, which integrated dark channel prior and the color correction algorithm based on channel histogram quantization. Firstly, for the underwater color image to be enhanced, underwater optical imaging model is established and the optimized and improved dark channel prior algorithm is used for image deblurring. Then, by analyzing the cumulative histogram of the deblurring image R, G, B three channels, the gray value of the each channel is quantified to achieve color correction of the image. The experimental results show that the proposed method can effectively eliminate the image blur caused by the scattering of light, improve the visual quality of the underwater image and restore color balance of the underwater image.