论文标题
基于多尺度的基于体素的解码，以增强大脑活动的自然图像重建
Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity
论文作者
论文摘要
通过功能磁共振成像（fMRI）监测的人脑活动的感知图像很难，尤其是对于自然图像。现有的方法通常会导致忠诚度低的模糊和难以理解的重建。在这项研究中，我们提出了一种新型的图像重建方法，其中现有用于对象解码和图像重建的方法合并在一起。这是通过将重建的图像调节到其解码图像类别中实现的。结果表明，我们的方法改善了重建图像的语义相似性，可以用作增强图像重建的一般框架。
Reconstructing perceived images from human brain activity monitored by functional magnetic resonance imaging (fMRI) is hard, especially for natural images. Existing methods often result in blurry and unintelligible reconstructions with low fidelity. In this study, we present a novel approach for enhanced image reconstruction, in which existing methods for object decoding and image reconstruction are merged together. This is achieved by conditioning the reconstructed image to its decoded image category using a class-conditional generative adversarial network and neural style transfer. The results indicate that our approach improves the semantic similarity of the reconstructed images and can be used as a general framework for enhanced image reconstruction.
