Graph filters for signal processing and machine learning on graphs
Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …
that reside on Euclidean domains, filters are the crux of many signal processing and …
Nonlinear Graph Wavelets via Medianfication
DB Tay - IEEE Transactions on Signal Processing, 2023 - ieeexplore.ieee.org
Graph wavelet transforms allow for the effective representation of signals that are defined
over irregular domains. The transform coefficients should be sparse, and encode salient …
over irregular domains. The transform coefficients should be sparse, and encode salient …
Compression of hyperspectral scenes through integer-to-integer spectral graph transforms
DEO Tzamarias, K Chow, I Blanes, J Serra-Sagristà - Remote Sensing, 2019 - mdpi.com
Hyperspectral images are depictions of scenes represented across many bands of the
electromagnetic spectrum. The large size of these images as well as their unique structure …
electromagnetic spectrum. The large size of these images as well as their unique structure …
Analysis of wavelet transform design via filter bank technique
The technique of filter banks has been extensively applied in signal processing in the last
three decades. It provides a very efficient way of signal decomposition, characterization, and …
three decades. It provides a very efficient way of signal decomposition, characterization, and …