摘要
由于水体对光的吸收和散射作用,水下图像普遍存在颜色失真和细节模糊这两种退化问题。为了得到色彩鲜明、细节清晰的水下图像,提出了一个基于多尺度注意力和对比学习的水下图像增强算法模型。该模型采用编码-解码结构作为基础框架,为了提取到更细粒度的特征,在编码部分设计了一个多尺度通道像素注意力模块,利用3个并行支路提取图像中不同层次的特征,然后将3条支路提取的特征进行融合输入到下一层编码器以及对应的解码层,提高网络特征提取以及细节增强的能力。最后,为了进一步提高增强图像的质量,引入对比学习训练网络。大量实验证明,本文算法增强后的图像色彩鲜明且细节信息保留较好。峰值信噪比和结构相似性指标的平均值最高可达到25.46和0.8946,与其他方法相比至少提高了4.4%和2.8%;水下彩色图像质量指标和信息熵的平均值最高为0.5802和7.6668,与其他方法相比均至少提高了2%;特征点匹配平均比原始图像多24个。
The two common degradations of underwater images are color distortion and blurred detail due to the absorption and dispersion of light by water.We propose an underwater image-enhancement algorithm model based on multi-scale attention and contrast learning to acquire underwater images with bright colors and clear details.The model adopts the encoding-decoding structure as the basic framework.To extract more fine-grained features,a multi-scale channel pixel attention module is designed in the encoder.The module uses three parallel branches to extract features at different levels in the image.In addition,the extracted features by the three branches are fused and introduced to the subsequent encoder and the corresponding decoding layer to improve the ability to extract network features and enhance details.Finally,a contrast-learning training network is introduced to improve the quality of enhanced images.Several experiments prove that the enhanced image by the proposed algorithm has vivid colors and complete detailed information.The average values of the peak signal-to-noise ratio and structural similarity index are up to 25.46 and 0.8946,respectively,and are increased by 4.4%and 2.8%,respectively,compared with the other methods.The average values of the underwater color image quality index and information entropy are 0.5802 and 7.6668,respectively,and are increased by at least 2%compared with the other methods.The number of feature matching points is increased by 24 compared to the original images.
作者
王悦
范慧杰
刘世本
唐延东
Wang Yue;Fan Huijie;Liu Shiben;Tang Yandong(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,Liaoning,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110016,Liaoning,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2024年第4期549-557,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(62273339,61991413)
中国科学院青年创新促进会项目(2019203)。
关键词
图像增强
注意力
多尺度
对比学习
image enhancement
attention
multi-scale
contrast learning
