论文标题
频域基于峰度的无参考图像质量评估明亮场显微镜图像
Frequency domain kurtosis-based no-reference image quality assessment for bright-field microscopy images
论文作者
论文摘要
在过去的几年中，图像处理研究人员花费了大量时间和精力来开发和完善图像质量评估算法。例如，明亮场显微镜会产生图像，其质量是用于一致评估的瓶颈。例如，当以不同的焦平面配置获取标本的一堆图像时，其中会有一组模糊或部分模糊的元素，从而损害了正确的评估。这项工作旨在提供图像质量评估度量，而没有参考图像进行比较，以检测整个堆栈中的模糊和清晰的图像，并选择最尖锐的图像，以进行进一步的融合过程。结果与图像集的主观标记的相关性表明，所提出的公制提供了合格图像的可靠识别以进行融合，并建议在其他现实世界中的问题中应用。
In the last few years, image processing researchers spent a substantial amount of time and effort developing and perfecting image quality assessment algorithms. Bright-field microscopy, for example, produces images whose quality is a bottleneck for consistent evaluation. For instance, when a stack of images of a specimen is acquired in different focal plane configurations, there will be a set of blurred or partially blurred elements in it, impairing proper evaluation. This work aims to provide an image quality assessment metric, without the presence of a reference image for comparison, to detect the blurred and sharp images among the whole set of the stack, and elect the sharpest ones for a further fusion process. The correlation of the results with subjective labeling of the image sets showed that the proposed metric offers reliable identification of the eligible images for fusion and suggests the application in other real-world problems.
