Photonic Data Science

Optical microscopy contributes to the ever-increasing progress in biological and biomedical studies, as it allows the implementation of minimally invasive experimental pipelines to translate the data of measured samples into valuable knowledge. Within these pipelines, reliable quality assessment must be ensured to validate the generated results. Image quality assessment is often applied with full-reference methods to estimate the similarity between the ground truth and the output images. However, current methods often show poor agreement with visual perception and lead to the generation of various full-reference metrics tailored to specific applications. Additionally, they rely on pixel-wise comparisons, emphasizing local intensity similarity while often overlooking comprehensive and interpretable image quality assessment. To address these issues, we have developed a multi-marker similarity method that compares standard quality markers, such as resolution, signal-to-noise ratio, contrast, and high-frequency components. The method computes a similarity score between the image and the ground truth for each marker and then combines these scores into an overall similarity estimate. This provides a full-reference estimate of image quality while extracting global quality features and detecting experimental artifacts. Ulti-marker similarity provides a reliable and interpretable method for image quality assessment and the generation of quality rankings. By focusing on the comparison of quality markers rather than direct image distances, the method enables reduced-reference implementations, where a single field of view is used as a benchmark for multiple measurements. This opens a way for reliable automatic evaluation of big datasets, typical of large biomedical studies, when manually assessing single images and defining the ground truth for each field of view is not feasible.