Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

By W. Zhang, Peixian Zhuang, Haihan Sun, Guohou Li, Sam Kwong, Chongyi Li. IEEE Transactions on Image Processing, 2022

775 citations recorded by the source corpus.

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size $1024\times 1024 \times 3$ on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3aaeec0df0073a18845dce7233ead0ffc03f362e

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. Chongyi Li
2. Chunle Guo
3. Chunli Zhu
4. Liheng Bian
5. Lintao Peng
6. Runmin Cong
7. Peixian Zhuang
8. Sam Kwong

In Media Technology

1. Chunli Zhu
2. Liheng Bian
3. Lintao Peng
4. Runmin Cong
5. Chongyi Li
6. Peixian Zhuang
7. Chunle Guo
8. Sam Kwong

Citations in the corpus

• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023, methodology): “The comparison methods includes 6 non-learning-based methods ( i . e ., GDCP [4], ACDE [25], HLRP [67], MLLE [68], UNTV [69], and SPDF [70]) and 8 deep learning-based methods ( i . e ., deep-sesr [59], FUnIE-GAN [5], WaterNet [2], UWCNN [46], JI-Net [56], ACPAB [53], TOPAL [55], and Ucolor [6]).”
• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, methodology): “Recently, fusion-based methods [35], [37] have taken advantage of the different feature maps to effectively integrate different enhanced versions to obtain a high-quality underwater image.”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “[38] proposed an UIE method based on minimal color loss and locally adaptive contrast enhancement.”