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Found 3 papers, 0 papers with code
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder
The architecture makes use of fractional calculus to calculate the gradients during the backpropagation process, as a result of which a new hyper-parameter in the form of fractional order ($\alpha$) has been introduced which can be tuned to get the best denoising performance.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images
However, ROF cannot completely remove the heavy tail of the noise distribution originating from the IN and thus the denoising performance can be suboptimal.
