spectrai: A deep learning framework for spectral data
원문 초록
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.
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Energy-Based Transformers are Scalable Learners and Thinkers
Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Learning Multi-scale Spatial-frequency Features for Image Denoising
A Real-time Endoscopic Image Denoising System
Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World Denoising
Single Image Denoising via a New Lightweight Learning-Based Model
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