  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.
