Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors

By Peixian Zhuang, Jiamin Wu, Fatih Murat Porikli, Chongyi Li. IEEE Transactions on Image Processing, 2022

386 citations recorded by the source corpus.

Underwater image enhancement aims at improving the visibility and eliminating color distortions of underwater images degraded by light absorption and scattering in water. Recently, retinex variational models show remarkable capacity of enhancing images by estimating reflectance and illumination in a retinex decomposition course. However, ambiguous details and unnatural color still challenge the performance of retinex variational models on underwater image enhancement. To overcome these limitations, we propose a hyper-laplacian reflectance priors inspired retinex variational model to enhance underwater images. Specifically, the hyper-laplacian reflectance priors are established with the $l_{1/2}$ -norm penalty on first-order and second-order gradients of the reflectance. Such priors exploit sparsity-promoting and complete-comprehensive reflectance that is used to enhance both salient structures and fine-scale details and recover the naturalness of authentic colors. Besides, the $l_{2}$ norm is found to be suitable for accurately estimating the illumination. As a result, we turn a complex underwater image enhancement issue into simple subproblems that separately and simultaneously estimate the reflection and the illumination that are harnessed to enhance underwater images in a retinex variational model. We mathematically analyze and solve the optimal solution of each subproblem. In the optimization course, we develop an alternating minimization algorithm that is efficient on element-wise operations and independent of additional prior knowledge of underwater conditions. Extensive experiments demonstrate the superiority of the proposed method in both subjective results and objective assessments over existing methods. The code is available at: https://github.com/zhuangpeixian/HLRP.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/95206ef486af8dc638865d667a8d6b09f876f1d9

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. Runmin Cong
4. Peixian Zhuang
5. Sam Kwong
6. Qingming Huang
7. Xipeng Pan
8. Ling Zhou

Citations in the corpus

• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, methodology): “Zhuang et al. [32] proposed a variational Retinex method based on the advantages of L 1 / 2 norm and L 2 norm, which better enhanced the contrast and texture details of underwater images, but introduced micro-red distortion.”
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023, methodology): “For example, GDCP [30] method introduces a severe green cast, HLRP [49] method makes the image reddish, and MLLE [50] method almost removes the color of the image.”