UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space

By Yudong Wang, Jichang Guo, Huan Gao, Huihui Yue. Signal processing. Image communication, 2021

375 citations recorded by the source corpus.

Abstract Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the last few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE2-Net) that efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN. To our best knowledge, this method is the first to use HSV color space for underwater image enhancement based on deep learning. UIEĈ2-Net is an end-to-end trainable network, consisting of three blocks as follow: a RGB pixel-level block implements fundamental operations such as denoising and removing color cast, a HSV global-adjust block for globally adjusting underwater image luminance, color and saturation by adopting a novel neural curve layer, and an attention map block for combining the advantages of RGB and HSV block output images by distributing weight to each pixel. Experimental results on synthetic and real-world underwater images show the good performance of our proposed method in both subjective comparisons and objective metrics. The code are available at https://github.com/BIGWangYuDong/UWEnhancement .

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/17cf9b85c348fc51a561929170ba6d3068c7bada

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Peixian Zhuang
2. Chongyi Li
3. Xipeng Pan
4. Ling Zhou
5. Weidong Zhang
6. Wenyi Zhao
7. Guohou Li
8. Chunle Guo

Citations in the corpus

• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, methodology): “UIECˆ2-Net [50] and PUIE-Net [55] are weaker than our WWPF method in terms of detail and contrast enhancement.”