Underwater Image Enhancement Frame Based on Color Balance and Feature Fusion

doi:10.19678/j.issn.1000-3428.0069600

Abstract

Abstract:

Owing to the complexity of underwater environments and the scattering and absorption of light, underwater images often suffer from issues such as image blurring, color distortion, and low visibility. To improve image quality, an image enhancement frame based on color balance and feature fusion is proposed. First, the Dark Channel Prior (DCP) parameters are optimized using a method that combines a quadtree search with light attenuation characteristics to address the issue of image blurring. Second, a differential compensation is applied to the two attenuated channels used for the deblurred image for achieving a color balanced image. Subsequently, to address the issues of detail loss and low contrast in color balanced images, guided filtering is employed to decompose the image and a nonlinear stretching function is introduced for improving the detail layer, resulting in a detail enhanced image. Using Contrast Limited Adaptive Histogram Equalization (CLAHE), normalized gamma correction is added to obtain contrast enhanced images. Finally, weight maps containing different features are extracted from the detail and contrast enhanced images and a multiscale pyramid strategy is adopted for fusion to obtain the final enhanced image. The experimental results demonstrate that compared to the references, this method improves the average values of underwater image quality metrics, average gradient, and patch-based contrast quality index by 17.6%, 76.4%, and 11.2%, respectively. This method exhibits robust performance in improving image quality and can achieve various image enhancement effects.

Key words: underwater image, Dark Channel Prior(DCP), color balance, contrast enhancement, details enhancement, feature fusion

摘要：

由于水下环境的复杂性和光线在水中传播时的散射和吸收, 水下图像经常遭受图像模糊、色彩失真和可见度低等问题的困扰。为改善图像的质量, 提出一种基于颜色均衡与特征融合的图像增强框架。首先, 利用四叉树搜索与光衰减特性相结合的方法对暗通道先验参数进行优化, 解决图像模糊的问题; 其次, 对去模糊图像的两个衰减通道进行差异化补偿, 获得颜色均衡图像; 然后, 针对颜色均衡图像细节丢失和对比度较低的问题, 采用引导滤波分解图像并引入非线性拉伸函数改善细节层, 得到细节增强图像; 在限制对比度自适应直方图均衡化的基础上添加归一化伽马校正, 得到对比度增强图像; 最后, 从细节增强图像和对比度增强图像中提取含有不同特征的权重图, 并采用多尺度金字塔策略进行融合, 得到最终增强图像。实验结果表明, 该方法在水下图像质量度量、平均梯度、基于斑块的对比度度量指数上的平均值相较于次优值算法分别提升了17.6%、76.4%和11.2%, 在提升图像质量方面具有良好的鲁棒性, 可以在不同场景下实现多种图像的增强效果。

关键词: 水下图像, 暗通道先验, 颜色均衡, 对比度增强, 细节增强, 特征融合

BAI Shaozhou, ZHANG Hao, ZHAO Jingbo, ZHANG Zhenkai, YUAN Hui. Underwater Image Enhancement Frame Based on Color Balance and Feature Fusion[J]. Computer Engineering, 2025, 51(10): 336-345.

白邵宙, 张浩, 赵景波, 张振楷, 元辉. 基于颜色均衡与特征融合的水下图像增强框架[J]. 计算机工程, 2025, 51(10): 336-345.

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Figures/Tables 14

Fig.1 The model of underwater optical imaging

Fig.2 Algorithm framework

Fig.3 Global background light estimationt

Fig.4 Deblurring image effect

Fig.5 Color balance image effect

Fig.6 Detail enhancement image effect

Fig.7 Enhancement effects in different color biases images and their tricolor histograms

Fig.8 Enhancement effects of blurred images in details

Fig.9 Enhancement effects of low illumination images

Fig.10 Comparison of objective evaluation index for different methods

Fig.11 experiment results of application test

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