基于刚好可察觉模糊和概率求和的无参考物镜图像清晰度度量

R. Ferzli, Lina Karam
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引用次数: 63

摘要

这项工作提出了一种基于感知的无参考客观图像清晰度/模糊度度量,通过将仅可注意模糊(JNB)的概念集成到概率求和模型中。与现有的客观无参考图像清晰度/模糊度指标不同,该指标能够预测不同内容图像的相对模糊度。提供了结果来说明提出的基于感知的锐度度量的性能。这些结果表明,提出的锐度度量与感知锐度相关良好。
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A No-Reference Objective Image Sharpness Metric Based on Just-Noticeable Blur and Probability Summation
This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur (JNB) into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness.
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