Deep learning computer vision techniques have achieved many successes in recent years across numerous imaging domains. However, the application of deep learning to spectral data remains a complex task due to the need for augmentation routines, specific architectures for spectral data, and significant memory requirements. Here we present spectrai, an open-source deep learning framework designed to facilitate the training of neural networks on spectral data and enable comparison between different methods. Spectrai provides numerous built-in spectral data pre-processing and augmentation methods, neural networks for spectral data including spectral (image) denoising, spectral (image) classification, spectral image segmentation, and spectral image super-resolution. Spectrai includes both command line and graphical user interfaces (GUI) designed to guide users through model and hyperparameter decisions for a wide range of applications.
翻译:近年来,深层学习计算机视觉技术在许多成像领域取得了许多成功,但是,深层学习应用于光谱数据仍是一项复杂的任务,因为需要增强常规、光谱数据的具体结构以及重要的内存要求。这里我们介绍光谱,这是一个开放源深层学习框架,旨在便利对神经网络进行光谱数据培训,便于对不同方法进行比较。 光谱提供许多内在的光谱数据预处理和增强方法,光谱数据的神经网络,包括光谱(光谱)分解、光谱(光谱)分类、光谱图像分解和光谱图像超分辨率。光谱包括指挥线和图形用户界面,旨在通过模型和超光谱决定,指导用户进行广泛的应用。