水下
度量(数据仓库)
图像质量
计算机视觉
计算机科学
对比度(视觉)
质量(理念)
人类视觉系统模型
人工智能
图像处理
图像(数学)
地质学
数据挖掘
物理
量子力学
海洋学
作者
Karen Panetta,Chen Gao,Sos С. Agaian
标识
DOI:10.1109/joe.2015.2469915
摘要
Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of human visual systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.
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