Author/Authors :
Tang, Wei Department of Biomedical Engineering - Hefei University of Technology - Hefei, China , Liu, Yu Department of Biomedical Engineering - Hefei University of Technology - Hefei, China , Zhang, Chao Department of Biomedical Engineering - Hefei University of Technology - Hefei, China , Cheng, Juan Department of Biomedical Engineering - Hefei University of Technology - Hefei, China , Peng, Hu Department of Biomedical Engineering - Hefei University of Technology - Hefei, China , Chen, Xun Department of Electronic Science and Technology - University of Science and Technology of China - Hefei, China
Abstract :
In the field of cell and molecular biology, green fluorescent protein (GFP) images provide functional information embodying the
molecular distribution of biological cells while phase-contrast images maintain structural information with high resolution.
Fusion of GFP and phase-contrast images is of high significance to the study of subcellular localization, protein functional
analysis, and genetic expression. +is paper proposes a novel algorithm to fuse these two types of biological images via generative
adversarial networks (GANs) by carefully taking their own characteristics into account. +e fusion problem is modelled as an
adversarial game between a generator and a discriminator. +e generator aims to create a fused image that well extracts the
functional information from the GFP image and the structural information from the phase-contrast image at the same time. +e
target of the discriminator is to further improve the overall similarity between the fused image and the phase-contrast image.
Experimental results demonstrate that the proposed method can outperform several representative and state-of-the-art image
fusion methods in terms of both visual quality and objective evaluation.
