论文标题
水下图像增强图像彩色度量
Underwater image enhancement with Image Colorfulness Measure
论文作者
论文摘要
由于水的吸收和散射效应，水下图像往往会遭受许多严重的问题，例如对比度低，灰色颜色和模糊含量。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。两个部分构成了整体模型。第一个是针对初步颜色校正的非参数层，然后第二部分由用于自适应改进的参数层组成，即通道的线性移位。有关更好的细节，对比度和色彩丰富，该增强网络通过像素级和特征级训练标准共同优化。通过对自然水下场景的广泛实验，我们表明该方法可以获得高质量的增强结果。
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 characteristiclevel training criteria. Through extensive experiments on natural underwater scenes, we show that the proposed method can get high quality enhancement results.
