We propose a method for restoring noisy and incomplete multivariate signals by learning multifold graphs. Spatial relationships among channels can be utilized to recover missing values and suppress noise. However, these relationships can vary across frequency components and observation windows while retaining common co...
Extensive experimental results on synthetic and real-world datasets show that the balanced graph learning method outperforms competing methods and enables reuse of spectral filters, wavelets, and graph neural nets constructed for positive graphs.
WREN is an LLIE neural network with double U-Net-like structures trained end-to-end with a scale-invariant loss function, which gives robustness against the illumination scaling, and achieves the state-of-the-art performance across multiple datasets.
Reina Kaneko, Junya Hara, Hiroshi Higashi et al.· International Conference on...· 0 citations
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