基于颜色校正和多尺度融合的水下图像增强

Underwater image enhancement based on color correction and multi-scale fusion

为了解决水下图像存在的颜色失真、细节模糊问题,本文算法以Unet网络为基本框架,同时在不同编码层中输入多尺度图像,通过融合上下层间的特征流来获得更优异的细节保持效果,实现了从粗到细的细节提取能力。此外,引入颜色校正模块和双重注意力模块,有效解决了水下图像色偏问题和细节恢复不均匀的问题。实验结果表明,在UFO、EUVP、UIEB数据集上,本文算法增强图像的PSNR和UIQM指标比原始图像平均分别提高了21.3%和25.6%。该算法能有效改善水下图像的视觉质量,在主观视觉和客观评价指标上优于其他算法。

To solve the problem of color distortion and detail blur in underwater images,the algorithm in this paper takes Unet network as the basic framework,inputs multi-scale images in different coding layers at the same time,fuses feature streams between upper and lower layers to obtain better detail preservation effect,and realizes the ability of extracting details from coarse to fine.In addition,the color correction module and dual attention module are introduced to effectively solve the problem of color deviation and uneven detail recovery in underwater images.The experimental results show that PSNR and UIQM indexes of the images enhanced by the proposed algorithm on UFO,EUVP and UIEB data sets increase by 21.3%and 25.6%respectively,compared with the original images.This algorithm can effectively improve the visual quality of underwater images,and is superior to other algorithms in subjective visual and objective evaluation indexes.