基于通道量化与红色先验融合的 水下光学图像清晰化

水下图像通常存在对比度低以及颜色失衡等现象，导致图像纹理信息不清晰，针对此类问题，提出基于通道量化与红色先验融合的水下光学图像清晰化方法。首先，设计两种输入图像版本，图像一通过颜色通道直方图量化重新分配像素值，调整对比度；图像二为实现色彩均衡，将红色通道先验代入成像模型，用于估计背景光、直接分量透射率和后向散射透射率。然后，针对各输入图像设计3种权重图，包括亮度图、饱和度图和显著图。最后，利用多尺度融合策略，将局部对比度提升和颜色校正图像与其归一化权重图进行融合。在多个数据库上通过主观和客观指标进行实验评价，结果表明，本文算法在呈现高对比度的同时，能够恢复出更多的色彩和细节信息，有效提升水下图像质量，与各经典及新颖算法相比具有优势。

Underwater optical image sharpening based on fusion of channelquantization and red prior

Underwater images usually have problems such as low contrast and color imbalance, which lead to unclear image texture information. Aiming at finding a solution, an underwater optical image sharpening method based on fusion of channel quantization and red prior is proposed. First, two input image versions are designed. Image 1 adjusts the image contrast by quantifying the color channel histogram and redistributes the pixel value. In image 2, in order to achieve color equalization, the red channel prior is substituted into the underwater imaging model to estimate the background light, direct component transmittance and backscattered transmittance. Then, three weight maps are designed for each input image, including brightness map, saturation map and saliency map. Finally, the multiscale fusion strategy is used to fuse the image after local contrast enhancement and color correction with its normalized weight map. The experimental evaluation carried out on multiple databases by subjective and objective indicators shows that the proposed algorithm can recover more color and detail information while presenting high contrast, effectively improve the quality of underwater images, and has advantages over other classical and novel algorithms.