Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performances with subjective and objective methods. However, the subjective evaluation is time-consuming and uneconomic for all images, while existing objective methods have limited capabilities for the newly-developed UIE approaches based on deep learning. To fill this gap, we propose an Underwater Image Fidelity (UIF) metric for objective evaluation of enhanced underwater images. By exploiting the statistical features of these images in CIELab space, we present the naturalness, sharpness, and structure indexes. Among them, the naturalness and sharpness indexes represent the visual improvements of enhanced images; the structure index indicates the structural similarity between the underwater images before and after UIE. We combine all indexes with a saliency-based spatial pooling and thus obtain the final UIF metric. To evaluate the proposed metric, we also establish a first-of-its-kind large-scale UIE database with subjective scores, namely Underwater Image Enhancement Database (UIED). Experimental results confirm that the proposed UIF metric outperforms a variety of underwater and general-purpose image quality metrics. The database and source code are available at https://github.com/z21110008/UIF .

中文翻译：

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UIF：水下图像增强的客观质量评估

由于复杂且不稳定的照明环境，水下成像很容易受到光散射、翘曲和噪声的影响。为了提高视觉质量，水下图像增强（UIE）技术得到了广泛的研究。最近的努力也有助于评估和比较 UIE 性能与主观和客观的方法。然而，主观评价对于所有图像来说都是耗时且不经济的，而现有的客观方法对于新开发的基于深度学习的 UIE 方法的能力有限。为了填补这一空白，我们提出了一种水下图像保真度 (UIF) 指标，用于客观评估增强的水下图像。通过利用这些图像在 CIELab 空间中的统计特征，我们展示了自然度、清晰度和结构指标。其中，自然度和锐度指标代表增强图像的视觉改进；结构指数表示UIE前后水下图像的结构相似度。我们将所有索引与基于显着性的空间池相结合，从而获得最终的 UIF 度量。为了评估提出的指标，我们还建立了首个具有主观评分的大型 UIE 数据库，即水下图像增强数据库 (UIED)。实验结果证实，所提出的 UIF 指标优于各种水下和通用图像质量指标。数据库和源代码可在 我们将所有索引与基于显着性的空间池相结合，从而获得最终的 UIF 度量。为了评估提出的指标，我们还建立了首个具有主观评分的大型 UIE 数据库，即水下图像增强数据库 (UIED)。实验结果证实，所提出的 UIF 指标优于各种水下和通用图像质量指标。数据库和源代码可在 我们将所有索引与基于显着性的空间池相结合，从而获得最终的 UIF 度量。为了评估提出的指标，我们还建立了首个具有主观评分的大型 UIE 数据库，即水下图像增强数据库 (UIED)。实验结果证实，所提出的 UIF 指标优于各种水下和通用图像质量指标。数据库和源代码可在https://github.com/z21110008/UIF .