59 citations · 258 across the 20 of their papers we have counts for
20 papers
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 Spatiotemporal Fusion of Satellite Images: A Constrained Convex Optimization Approach
Ryosuke Isono, Kazuki Naganuma, Shunsuke Ono
This paper proposes a novel spatiotemporal (ST) fusion framework for satellite images, named Robust Optimization-based Spatiotemporal Fusion (ROSTF). ST fusion is a promising appro…
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…
Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization
Kazuki Naganuma, Shunsuke Ono
Hyperspectral (HS) unmixing is the process of decomposing an HS image into material-specific spectra (endmembers) and their spatial distributions (abundance maps). Existing unmixin…
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications
Kazuki Naganuma, Shunsuke Ono
This paper proposes a method for designing diagonal preconditioners for a preconditioned primal-dual splitting method (P-PDS), an efficient algorithm that solves nonsmooth convex o…
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization
Saori Takeyama, Shunsuke Ono
The paper proposes a new high spatial resolution hyperspectral (HR-HS) image estimation method based on convex optimization. The method assumes a low spatial resolution HS (LR-HS)…
Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
Takayuki Nagata, Keigo Yamada, Taku Nonomura +3
The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based…
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…
Guided Facial Skin Color Correction
Keiichiro Shirai, Tatsuya Baba, Shunsuke Ono +3
This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to backgrou…
A General Destriping Framework for Remote Sensing Images Using Flatness Constraint
Kazuki Naganuma, Shunsuke Ono
Removing stripe noise, i.e., destriping, from remote sensing images is an essential task in terms of visual quality and subsequent processing. Most existing destriping methods are…
Data-driven sparse sensor placement based on A-optimal design of experiment with ADMM
Takayuki Nagata, Taku Nonomura, Kumi Nakai +3
The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of mult…
On The Synergy Between Nonconvex Extensions of The Tensor Nuclear Norm for Tensor Recovery
Kaito Hosono, Shunsuke Ono, Takamichi Miyata
Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank--e.g. the CP rank and…
Randomized Subspace Newton Convex Method Applied to Data-Driven Sensor Selection Problem
Taku Nonomura, Shunsuke Ono, Kumi Nakai +1
The randomized subspace Newton convex methods for the sensor selection problem are proposed. The randomized subspace Newton algorithm is straightforwardly applied to the convex for…
Epigraphical Relaxation for Minimizing Layered Mixed Norms
Seisuke Kyochi, Shunsuke Ono, Ivan Selesnick
This paper proposes an epigraphical relaxation (ERx) technique for non-proximable mixed norm minimization. Mixed norm regularization methods play a central role in signal reconstru…
A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization
Saori Takeyama, Shunsuke Ono, Itsuo Kumazawa
We propose a new constrained optimization approach to hyperspectral (HS) image restoration. Most existing methods restore a desirable HS image by solving some optimization problem,…
TrendNets: Mapping Emerging Research Trends From Dynamic Co-Word Networks via Sparse Representation
Marie Katsurai, Shunsuke Ono
Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary researc…
Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation
Yuhei Kaneko, Shogo Muramatsu, Hiroyasu Yasuda +4
This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse coding. EDMD is a…
Efficient Constrained Signal Reconstruction by Randomized Epigraphical Projection
Shunsuke Ono
This paper proposes a randomized optimization framework for constrained signal reconstruction, where the word "constrained" implies that data-fidelity is imposed as a hard constrai…
Fast Singular Value Shrinkage with Chebyshev Polynomial Approximation Based on Signal Sparsity
Masaki Onuki, Shunsuke Ono, Keiichiro Shirai +1
We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA). Many signal processing problems require iterative application…
Discrete-Valued Control by Sum-of-Absolute-Values Optimization
Takuya Ikeda, Masaaki Nagahara, Shunsuke Ono
In this paper, we propose a new design method of discrete-valued control for continuous-time linear time-invariant systems based on sum-of-absolute-values (SOAV) optimization. We f…