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In the last few years, image processing researchers spent a substantialamount of time and effort developing and perfecting image quality assessmentalgorithms. Bright-field microscopy, for example, produces images whose qualityis a bottleneck for consistent evaluation. For instance, when a stack of imagesof a specimen is acquired in different focal plane configurations, there willbe a set of blurred or partially blurred elements in it, impairing properevaluation. This work aims to provide an image quality assessment metric,without the presence of a reference image for comparison, to detect the blurredand sharp images among the whole set of the stack, and elect the sharpest onesfor a further fusion process. The correlation of the results with subjectivelabeling of the image sets showed that the proposed metric offers reliableidentification of the eligible images for fusion and suggests the applicationin other real-world problems.

Frequency domain kurtosis-based no-reference image quality assessment for bright-field microscopy images