Underwater biological image enhancement based on lightweight neural networks
This study proposes a lightweight underwater image restoration method to obtain high-quality biological images and address issues such as color cast and low contrast.The process compensates for color distortion in underwater images through global averaging and max pooling,reducing the influence of irrelevant features in the input images and enhancing the model′s focus on key features.It introduces ghost convolution to reduce redundancy in feature maps and model parameters.Additionally,dynamic convolution is used to adjust the brightness and contrast of images in three color spaces adaptively.The results show that the proposed model outperforms contrast methods based on non-physical and physical models in terms of peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM),with values of 24.298 and 0.891,respectively,on the Enhanced Underwater Vision Perception(EUVP)underwater scene dataset.Compared to the deep learning models Shallow-Uwnet and Water-Net on the EUVP underwater biological image dataset,the proposed model achieves computational efficiency of only 0.27%and 0.04%,respectively.The improved images show an SSIM improvement of 3.77%and 6.72%,compared to the two aforementioned models.By directly adjusting pixel values to restore underwater image colors,this model occupies only 41 KB of storage space,making it suitable for deployment on underwater robots.This study demonstrates that the proposed model effectively enhances underwater biological images,and due to its low computational load and storage requirements,it contributes to the accurate support of ocean data and promotes the development of marine resources.
国家科技期刊平台
NSTL
万方数据
维普
