5 papers
Generalized Graph Signal Sampling by Difference-of-Convex Optimization
Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono
We propose a comprehensive framework for the generalized sampling and recovery of generalized graph signals by leveraging difference-of-convex (DC) optimization. A fundamental chal…
Sampling Method for Generalized Graph Signals with Pre-selected Vertices via DC Optimization
Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono
This paper proposes a method for vertex-wise flexible sampling of a broad class of graph signals, designed to attain the best possible recovery based on the generalized sampling th…
Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping
Shingo Takemoto, Kazuki Naganuma, Shunsuke Ono
This paper proposes a novel regularization method, named Spatio-Spectral Structure Tensor Total Variation (S3TTV), for denoising and destriping of hyperspectral (HS) images. HS ima…
Robust Foreground-Background Separation for Severely-Degraded Videos Using Convolutional Sparse Representation Modeling
Kazuki Naganuma, Shunsuke Ono
This paper proposes a foreground-background separation (FBS) method with a novel foreground model based on convolutional sparse representation (CSR). In order to analyze the dynami…
Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data
Eisuke Yamagata, Kazuki Naganuma, Shunsuke Ono
We propose a time-varying graph signal recovery method for estimating the true time-varying graph signal from corrupted observations by leveraging dynamic graphs. Most of the conve…