Lightweight Image Preprocessing Model for Improving Microorganism Detection in Microscopic Scenes

This paper presents a study aimed at improving the detection quality of small-sized microorganisms under challenging microscopic conditions through the application of a lightweight combined image preprocessing model. We focused on the task of detecting diplococci in images obtained through dynamic sample microscopy. The proposed approach employs predefined filters for image preprocessing, combined with the prediction of their applicability. This enables the elimination of artifacts such as blurred boundaries and indistinct edges, which are often encountered when working with small-sized objects in nonfixed scenes. The application of the proposed model has significantly improved detection quality compared to traditional methods.