School of Electronic and Information Engineering, Liaoning Technical University, Huludao, China.
PLoS One. 2022 Sep 26;17(9):e0275107. doi: 10.1371/journal.pone.0275107. eCollection 2022.
Low contrast, poor color saturation, and turbidity are common phenomena of underwater sensing scene images obtained in highly turbid oceans. To address these problems, we propose an underwater image enhancement method by combining Retinex and transmittance optimized multi-scale fusion framework. Firstly, the grayscale of R, G, and B channels are quantized to enhance the image contrast. Secondly, we utilize the Retinex color constancy to eliminate the negative effects of scene illumination and color distortion. Next, a dual transmittance underwater imaging model is built to estimate the background light, backscattering, and direct component transmittance, resulting in defogged images through an inverse solution. Finally, the three input images and corresponding weight maps are fused in a multi-scale framework to achieve high-quality, sharpened results. According to the experimental results and image quality evaluation index, the method combined multiple advantageous algorithms and improved the visual effect of images efficiently.
低对比度、色彩饱和度差和浑浊是高度浑浊海洋中获得的水下感测场景图像的常见现象。针对这些问题，我们提出了一种结合反射率和透射率优化多尺度融合框架的水下图像增强方法。首先，量化 R、G 和 B 通道的灰度值以增强图像对比度。其次，利用反射率颜色恒常性消除场景照明和颜色失真的负面影响。接下来，构建双透射率水下成像模型来估计背景光、后向散射和直接分量透射率，通过逆解得到去雾图像。最后，在多尺度框架中融合三个输入图像及其对应的权重图，以实现高质量、锐化的结果。根据实验结果和图像质量评估指标，该方法结合了多种优势算法，有效地提高了图像的视觉效果。
