In image denoising and reconstruction problems, it is useful to monotonically quantify the distortions in images representing the same scene. For this purpose, we propose a training-free no-reference image distortion assessment method for both blur and noise. The method is based on the observation that the structural similarity between an input image and its shifted-and-noised copy is related to the levels of blur and noise distorting the input image. Computing a noised copy would require a random number generation, but assuming virtual noise independent from the input image, we derived the method that does not require computing actual noised copy. In our experiments of assessing the singly/multiply distorted images representing the same scene, the proposed method generally showed better monotonicity than competing state-of-the-art methods. The proposed method runs about 80 times faster than those competing methods and it can assess local regions of the input image, which makes it useful in spatially adaptive denoising applications.

中文翻译：

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噪声下的结构韧性：模糊和噪声的有效无参考图像失真评估

在图像去噪和重建问题中，单调量化表示同一场景的图像中的失真很有用。为此，我们提出了一种用于模糊和噪声的免训练无参考图像失真评估方法。该方法基于以下观察：输入图像与其移位和噪声副本之间的结构相似性与使输入图像失真的模糊和噪声水平有关。计算噪声副本需要生成随机数，但假设独立于输入图像的虚拟噪声，我们推导出不需要计算实际噪声副本的方法。在我们评估代表同一场景的单/多失真图像的实验中，所提出的方法通常比竞争的最新方法表现出更好的单调性。