Most traditional single image deblurring methods before deep learning adopt a coarse-to-fine scheme that estimates sharp at coarse scale and progressively refines it finer scales. While this has also been adopted in several learning-based approaches, recently number of single-scale approaches have introduced showing superior performance to previous terms quality computation time. In paper, we r...

Existing automatic sleep stage detection methods predominantly use convolutional neural network classifiers (CNNs) trained on features extracted from single-modality signals such as electroencephalograms (EEG). On the other hand, multimodal approaches propose very complexly stacked structures with multiple CNN branches merged by a fully connected layer. It leads to high computational and data r...