Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.
PLoS One. 2023 Feb 2;18(2):e0281093. doi: 10.1371/journal.pone.0281093. eCollection 2023.
At present, there are some problems in underwater low light image, such as low contrast, blurred details, color distortion. In the process of low illumination image enhancement, there are often problems such as artifacts, loss of edge details and noise amplification in the enhanced image. In this paper, we propose an underwater low-light enhancement algorithm based on U-shaped generative adversarial network, combined with bright channel prior and attention mechanism, to address the problems. For the problems of uneven edges and loss of details that occurred in traditional enhanced images, we propose a two-channel fusion technique for the input channel. Aiming at the problems of brightness, texture and color distortion in enhanced images, we propose a feature extraction technique based on the attention mechanism. For the problems of noise in enhanced output images, we propose a multi-loss function to constrain the network. The method has a wide range of applications in underwater scenes with large depth. This method can be used for target detection or biological species identification in underwater low light environment. Through the enhancement experiment of underwater low light image, the proposed method effectively solves the problems of low contrast, blurred details, color distortion, etc. of underwater low light image. Finally, we performed extensive comparison experiments and completed ablation experiments on the proposed method. The experimental results show that the proposed method is optimal in human visual experience and underwater image quality evaluation index.
目前水下微光图像存在对比度低、细节模糊、颜色失真等问题。在低照度图像增强过程中，增强后的图像往往存在伪影、边缘细节丢失和噪声放大等问题。针对传统增强图像出现的边缘不均匀和细节丢失等问题，本文提出了一种基于 U 型生成对抗网络的水下微光增强算法，结合亮通道先验和注意力机制来解决这些问题。针对增强图像出现的亮度、纹理和颜色失真问题，本文提出了一种基于注意力机制的特征提取技术。针对增强输出图像中的噪声问题，本文提出了一种多损失函数来约束网络。该方法在大深度的水下场景中具有广泛的应用。该方法可用于水下微光环境中的目标检测或生物物种识别。通过水下低光图像的增强实验，该方法有效地解决了水下低光图像对比度低、细节模糊、颜色失真等问题。最后，我们对所提出的方法进行了广泛的对比实验和消融实验。实验结果表明，所提出的方法在人眼视觉体验和水下图像质量评价指标方面是最优的。
