一种用于评估卫星图像清晰度的新型无参考图像质量度量
A Novel No-Reference Image Quality Metric For Assessing Sharpness In Satellite Imagery
AI总结：
本文提出一种新型无参考图像清晰度度量，通过测量显著边缘梯度归一化衰减率评估卫星图像清晰度，对噪声等因素鲁棒且计算高效，优于常规方法。
摘要：
本研究引入了一种新型无参考图像质量度量，旨在评估图像清晰度。该度量设计为对噪声、曝光、对比度及图像内容的变化具有鲁棒性，通过测量沿显著边缘的梯度归一化衰减率，提供了一种无需参考图像的清晰度客观评估方法。该度量主要为卫星图像开发，以符合人类对清晰度的视觉感知，支持卫星编队的监测与质量表征。在各种图像类型和操作条件下，它在与人类感知的一致性方面展现出显著的实用性和优越的性能。与常规度量不同，这种启发式方法提供了一种将图像从较低清晰度到较高清晰度进行评分的方式，使其成为增强质量评估过程的可靠且通用的工具，无需原始或真值比较。此外，与深度学习分析相比，该度量在计算上十分高效，确保了更快且更具资源效益的清晰度评估。
英文摘要：
This study introduces a novel no-reference image quality metric aimed at assessing image sharpness. Designed to be robust against variations in noise, exposure, contrast, and image content, it measures the normalized decay rate of gradients along pronounced edges, offering an objective method for sharpness evaluation without reference images. Primarily developed for satellite imagery to align with human visual perception of sharpness, this metric supports monitoring and quality characterization of satellite fleets. It demonstrates significant utility and superior performance in consistency with human perception across various image types and operational conditions. Unlike conventional metrics, this heuristic approach provides a way to score images from lower to higher sharpness, making it a reliable and versatile tool for enhancing quality assessment processes without the need for pristine or ground truth comparison. Additionally, this metric is computationally efficient compared to deep learning analysis, ensuring faster and more resource-effective sharpness evaluations.
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