Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement
Vidas Raudonis1, Agne Paulauskaite-Taraseviciene2, Kristina Sutiene3
1Department of Automation, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania.
Background:
Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time.
Methods:
Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image.
Results:
The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques-Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization.
Conclusion:
Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.
