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Color and sharpness assessment of single image dehazing

机译：单个图像去雾的颜色和清晰度评估

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摘要

Image dehazing is the process of enhancing a color image of a natural scene that contains an undesirable veil of fog for visualization or as a pre-processing step for computer vision systems. In this work, we investigate the performances of eleven state-of-the-art image quality metrics in evaluating dehazed images, and discuss challenges in designing an efficient dehazing evaluation metric. This is done through a composite study based on the agreement between subjective and objective evaluations. Accordingly, we evaluate five state-of-the-art dehazing algorithms. We use two semi-indoor scenes, degraded with several levels of fog. One important aspect of these scenes is that the fog-free images are available and can therefore serve as ground-truth data for dehazing methods evaluation. This study shows that the best working dehazing method depends on the density of fog. There seems to be a clear distinction between what people perceive as good quality in terms of color restoration and in terms of sharpness restoration. Most metrics show limitations in providing proper quality prediction of dehazing. According to the introduction and analysis, a contribution of this work is to point out the flaws in the evaluation and development of dehazing methods. Our observations might be considered when designing efficient methods and metrics dedicated to image dehazing.
机译：图像去雾是增强自然场景的彩色图像的过程，该图像包含令人讨厌的雾蒙蒙以进行可视化或作为计算机视觉系统的预处理步骤。在这项工作中，我们调查了11个最先进的图像质量指标在评估除雾图像中的性能，并讨论了在设计有效除雾评估指标时面临的挑战。这是通过基于主观评估与客观评估之间一致性的综合研究完成的。因此，我们评估了五种最先进的除雾算法。我们使用两个半室内场景，这些场景会随着多级雾而退化。这些场景的一个重要方面是无雾图像可用，因此可以用作去雾方法评估的真实数据。这项研究表明，最佳的除雾方法取决于雾的密度。在色彩还原和清晰度还原方面，人们认为优质的产品之间似乎存在明显的区别。大多数指标在提供正确的除雾质量预测方面显示出局限性。根据引言和分析，这项工作的目的是指出除雾方法评估和开发中的缺陷。在设计用于图像去雾的有效方法和指标时，可能会考虑我们的观察结果。

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