Microscopy image acquisition is frequently limited by a shallow depth of field (DoF), restricted dynamic range (DR), uneven illumination, and noise, particularly when imaging transparent or weakly textured biological specimens. This work presents MFusionJ (MFJ), an open-source ImageJ/Fiji plugin for microscopic multi-focus image fusion (MFIF) and multi-exposure image fusion (MEIF). The proposed method combines two-scale decomposition (TSD), edge-preserving filtering (EPF), and weighted average fusion (WAF) to fuse base and detail layers separately using refined weight maps. MFJ was evaluated on heterogeneous microscopy datasets, including nine diatom MFIF datasets, 100 cyanobacteria stacks, 93 cervical cytology stacks, and ten diatom MEIF datasets. The comparison includes publicly or commercially available tools and methods that can be directly applied to microscopy image stacks, namely EDF, Zerene Stacker, Helicon Focus, Photomatix Pro, BLT-TM, and a CNN-based fusion baseline. In the diatom MFIF experiments, MFJ achieved the best average QAB/F and NAB/F scores, with values of 0.821 and 0.010, respectively. For cyanobacteria, MFJ obtained the best average QAB/F, NAB/F, and PIQE scores, with values of 0.543, 0.005, and 9.984, respectively. For cervical cytology, MFJ achieved the best average QAB/F, LAB/F, NAB/F, and BRISQUE scores, with values of 0.935, 0.065, 0.000, and 30.472, respectively. For MEIF, MFJ achieved the best PIQE score and average performance comparable to that of the leading methods in reference-based metrics. These results show that MFJ provides a reproducible, training-free, and user-accessible solution for enhancing DoF, preserving fine details, and reducing fusion artifacts in heterogeneous microscopy imaging scenarios.
Singh et al. (Sat,) studied this question.