基于生成对抗网络的深海图像增强算法

Deep-sea image enhancement algorithm based on generativeadversarial network

在复杂的深海环境中提高图像的质量和可视化效果对水下科学研究和工程应用具有重要意义。针对深海特殊环境导致深海数据集稀缺,以及深海图像存在的色彩失真、对比度低等问题本文构建了一个成对的深海图像数据集DSIEB,并在此基础上提出了一种结合DC注意力和MSDR多尺度密集残差的生成对抗网络DM-GAN算法。首先,在网络跳跃连接部分构建DC双重通道注意力机制,用于加强通道间联系,提取图像细节纹理特征。其次,在生成器结构中嵌入MSDR多尺度密集残差块,提高对局部信息的关注和特征重用能力。最后,重构新的损失函数,引入平滑保真度SF损失,从多个角度引导网络学习原始图像到目标图像的映射。通过在自建数据集DSIEB上进行实验验证,并与7种先进水下图像增强算法进行对比实验,实验结果表明本文所提算法具有更强的泛化能力,适应于多样性的深海图像。

Improving image quality and visualization in complex deep-sea environments is of great importance for underwater scientific research and engineering applications.In order to solve the problems of scarcity of deep-sea datasets caused by the special environment of the deep-sea,as well as the problems of color distortion and low contrast of deep-sea images,a paired deep-sea image dataset DSIEB was created,and on this basis a generative adversarial DM-GAN was built algorithm proposed for networks combining DC attention and MSDR multi-scale dense residuals.First,the DC dual-channel attention mechanism is built in the network hopping connection part to strengthen the connection between channels and extract the texture features of image details.Second,the MSDR multi-scale residual block is embedded in the generator structure to improve the attention is on local information and the ability to reuse features.Finally,a new loss function is reconstructed and the smoothing fidelity SF loss is introduced to guide the network to learn the mapping from the original image to the target image from multiple perspectives.Experiments are carried out on the self-built dataset DSIEB,and compared with seven advanced underwater image enhancement algorithms.The experimental results show that the proposed algorithm has stronger generalization ability and is suitable for various deep-sea images.