Deep learning methods are state-of-the-art for spectral image (SI) computational tasks. However, these methods are constrained in their performance since available datasets are limited due to the highly expensive and long acquisition time. Usually, data augmentation techniques are employed to mitigate the lack of data. Surpassing classical augmentation methods, such as geometric transformations, GANs enable diverse augmentation by learning and sampling from the data distribution. Nevertheless, GAN-based SI generation is challenging since the high-dimensionality nature of this kind of data hinders the convergence of the GAN training yielding to suboptimal generation. To surmount this limitation, we propose low-dimensional GAN (LD-GAN), where we train the GAN employing a low-dimensional representation of the {dataset} with the latent space of a pretrained autoencoder network. Thus, we generate new low-dimensional samples which are then mapped to the SI dimension with the pretrained decoder network. Besides, we propose a statistical regularization to control the low-dimensional representation variance for the autoencoder training and to achieve high diversity of samples generated with the GAN. We validate our method LD-GAN as data augmentation strategy for compressive spectral imaging, SI super-resolution, and RBG to spectral tasks with improvements varying from 0.5 to 1 [dB] in each task respectively. We perform comparisons against the non-data augmentation training, traditional DA, and with the same GAN adjusted and trained to generate the full-sized SIs. The code of this paper can be found in https://github.com/romanjacome99/LD_GAN.git

本文提出一种基于低维 GAN 的数据增强方法，能够有效提高压缩光谱成像、光谱图像超分辨率以及 RGB 到光谱任务的表现，并在传统数据增强方法和同样应用 GAN 的方法的基础上取得了 0.5 到 1 [dB] 的改进。

LD-GAN: 低维度的生成对抗网络用于带有方差正则化的光谱图像生成