DCNN-GAN: Reconstructing Realistic Image from fMRI

Understand

Visualizing the perceptual content by analyzing human functional magnetic resonance imaging (fMRI) has been an active research area.

• However, due to its high dimensionality, complex dimensional structure, and small number of samples available, reconstructing realistic images from fMRI remains challenging.
• Recently with the development of convolutional neural network (CNN) and generative adversarial network (GAN), mapping multi-voxel fMRI data to complex, realistic images has been made possible.
• In this paper, we propose a model, DCNN-GAN, by combining a reconstruction network and GAN.