基于多尺度注意力融合和卷积神经网络的水下图像恢复

Underwater image restoration based on multi-scale attention fusion and convolutional neural network

由于水中悬浮粒子对光的吸收和散射,导致原始水下图像清晰度低、细节模糊和颜色失真,针对这些问题,提出了一种基于多尺度注意力融合和卷积神经网络CNN的水下图像恢复方法。利用注意力机制构造SC(Space channel)模块,通过在多尺度特征提取中加入SC模块,可以有效地提取图像中的信息,实现图像清晰度的提高和颜色校正。利用拉普拉斯算子构造多项损失函数,进一步增强图像细节特征,使得恢复后的图像质量得到显著提升。将本文方法与其他方法在两个测试集上进行定性和定量的对比,实验结果表明,本文方法恢复后的图像在图像清晰度、细节增强和颜色校正方面都优于其他方法。

Due to the absorption and scattering of light by suspended particles in water,the original underwater image has a low definition,fuzzy details,and color distortion.To solve these problems,an underwater image recovery method based on multi-scale attention fusion and Convolutional Neural Network(CNN)is proposed.First,the Space Channel(SC)module is constructed by using the attention mechanism.Then,by adding the SC module into the multi-scale feature extraction,the information in the image can be extracted effectively,and the image sharpness and color correction can be realized.Finally,the Laplacian operator is used to construct the multiple loss functions to further enhance the detail features of the image,so that the recovered image quality can be significantly improved.The method in this paper is compared with other methods qualitatively and quantitatively on the two test sets.The experimental results show that this method is superior to other methods in image sharpness,detail enhancement,and color correction.