人工智能
水下
计算机视觉
颜色校正
亮度
计算机科学
稳健性(进化)
颜色归一化
失真(音乐)
直方图均衡化
RGB颜色模型
颜色直方图
彩色图像
直方图
图像处理
图像(数学)
地质学
基因
海洋学
生物化学
计算机网络
化学
放大器
带宽(计算)
作者
Haofeng Hu,Shuping Xu,Yazhuo Zhao,Hongyi Chen,Shiyao Yang,Hedong Liu,Jingsheng Zhai,Xiaobo Li
标识
DOI:10.1109/joe.2023.3306591
摘要
Underwater images always suffer from color distortion, contrast decrease, and detail blur due to the selective absorption and scattering of water, which significantly limits their applications. In this article, we introduce an effective underwater image enhancement method to improve image quality, i.e., correcting color distortion, enhancing contrast, and enriching details. Specifically, we first use a color-cast factor to classify underwater images into no-color-cast and color-cast images; the latter is divided into three color shifts to help apply adaptive color correction approaches for images with different color shifts. Based on the color-corrected results, we enhance their luminance information and enrich details via a well-designed histogram equalization algorithm and a multiscale detail superposition algorithm. We finally combine fused luminance information and corrected color information to output the desired images with improved quality. Experiments on four representative underwater benchmarks validate our method's robustness to different categories of underwater images, as well as its superiority compared with state-of-the-art methods. The proposed underwater imaging solution holds significant potential for practical applications in ocean engineering, e.g., ocean vision, underwater tracking, target identification, and location.
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