Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfulness, this enhancement network is jointly optimized by the pixel-level and characteristic-level training criteria. Through extensive experiments on natural underwater scenes, we show that the proposed method can get high quality enhancement results.

由于水的吸收和散射作用，水下图像往往会遭受许多严重问题的困扰，例如对比度低，颜色变灰和内容模糊。为了提高水下图像的视觉质量，我们提出了一种新颖的增强模型，这是一种可训练的端到端神经模型。整个模型由两部分组成。第一个是用于初步色彩校正的非参数层，然后第二部分由用于自适应优化的参数层组成，即通道方向的线性位移。为了获得更好的细节，对比度和色彩，该增强网络通过像素级和特征级训练标准共同进行了优化。通过对水下自然场景的大量实验，我们证明了该方法能够获得高质量的增强效果。