# DCNN-GAN: Reconstructing Realistic Image from fMRI

arXiv: https://arxiv.org/abs/1901.07368 · HTML: https://mlsanity.com/paper/1901.07368/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2019.

## Built on by (most cited first, 16 of 16)

- [MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data](https://mlsanity.com/paper/2403.11207/) (2024)
- [Decoding natural image stimuli from fMRI data with a surface-based convolutional network](https://mlsanity.com/paper/2212.02409/) (2022)
- [BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding](https://mlsanity.com/paper/2302.12971/) (2023)
- [Scaling laws for decoding images from brain activity](https://mlsanity.com/paper/2501.15322/) (2025)
- [NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery](https://mlsanity.com/paper/2506.06898/) (2025)
- [More Than Meets the Eye: Self-Supervised Depth Reconstruction From Brain Activity](https://mlsanity.com/paper/2106.05113/) (2021)
- [Neural Photofit: Gaze-based Mental Image Reconstruction](https://mlsanity.com/paper/2108.07524/) (2021)
- [Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer](https://mlsanity.com/paper/2510.25976/) (2025)
- [Generative Adversarial Networks for Brain Images Synthesis: A Review](https://mlsanity.com/paper/2305.15421/) (2023)
- [Facial Image Reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency](https://mlsanity.com/paper/2207.01011/) (2022)
- [Using Deep Learning for Visual Decoding and Reconstruction from Brain Activity: A Review](https://mlsanity.com/paper/2108.04169/) (2021)
- [Double-Flow GAN model for the reconstruction of perceived faces from brain activities](https://mlsanity.com/paper/2312.07478/) (2023)
- [HAIFAI: Human-AI Interaction for Mental Face Reconstruction](https://mlsanity.com/paper/2412.06323/) (2024)
- [Mapping fMRI Signal and Image Stimuli in an Artificial Neural Network Latent Space: Bringing Artificial and Natural Minds Together](https://mlsanity.com/paper/2503.19923/) (2025)
- [StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation](https://mlsanity.com/paper/2605.02586/) (2026)
- [Brain-IT-VQA: From Brain Signals to Answers](https://mlsanity.com/paper/2605.29588/) (2026)

## Abstract (arXiv)

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. We utilize the CNN for hierarchical feature extraction and the DCNN-GAN to reconstruct more realistic images. Extensive experiments have been conducted, showing that our method outperforms previous works, regarding reconstruction quality and computational cost.