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Xavier Bunlon - IEEE Xplore Author Profile

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Author details

Xavier Bunlon

Also published under: X. Bunlon

Publications

21

Citations

106

Publications by Year

20052024

Co-Authors:

Emmanuel AguerreFatima AjebbarG. AndrieuAlain AzoulaySolange Bertuol

Show All Co-Authors (48)

Xavier Bunlon

Also published under: X. Bunlon

Affiliation

Renault

Technocentre

Guyancourt, France

Publication Topics

Impedance Curves,
Method Of Moments,
Simulation Model,
Cable System,
Current Distribution,
Current Probe,
Early Design Stages,
Electromagnetic Compatibility,
Ground Wire,
Matrix Inversion Lemma,
Partial Element Equivalent Circuit,
Resonance Frequency

Biography

Xavier Bunlon was born in Saint-Dizier, France, on November 9, 1977. He received the French engineering degree in electronics from Suplec, Gif-sur-Yvette, France, in 2001 and the Ph.D. degree in microwave and electronics from the University Paul Sabatier, Toulouse, France, in 2004, in cooperation with Renault Group and the Electromagnetism and Radar Department, ONERA, Toulouse.,In 2004, he joined Renault Group, Boulogne-Billancourt, France. From 2014 to 2019, he was an EMC and Antenna Team Manager. Since May 2019, he has been an EMC Expert.,Dr. Bunlon is a member of ISO and CISPR-D International Committees.(Based on document published on 18 March 2020).

Publications

21

Citations

106

Publications by Year

20052024

Co-Authors:

Emmanuel Aguerre
Fatima Ajebbar
G. Andrieu
Alain Azoulay
Solange Bertuol

Show All Co-Authors (48)

Author's Published Works

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Showing 1-25 of 100 results

Conferences (71)

Journals (29)

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Tokyo Institute of Technology(21)
Tokyo Institute of Technology, Tokyo, Japan(9)
Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan(5)
Tokyo Institute of Technology, Japan(5)
The University of Kitakyushu, Fukuoka, Japan(4)
IEEE Transactions on Signal Processing(6)
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(5)
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(4)
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(4)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(4)
IEEE(100)
Code(1)
Media(1)
Rhodes Island, Greece(5)
Brighton, UK(4)
Calgary, AB, Canada(4)
Seoul, Korea, Republic of(4)
Taipei, Taiwan(4)
Proximal Operator(69)
Convex Optimization Problem(46)
Additive Noise(33)
Convex Optimization(33)
Optimization Problem(26)

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Results

Stable and Lightweight Deep Primal-Dual Unrolling for Constrained Image Restoration with Convolutional Sparse Coding

Takafumi Ueki;Kazuki Naganuma;Shunsuke Ono

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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This paper proposes an image restoration method using a convolutional sparse coding (CSC) unrolling network with a box constraint and total variation. Unlike conventional deep unrolling methods, the proposed method constructs an interpretable lightweight network with restoration stability. Specifically, we design a new constrained convex optimization problem that incorporates CSC, a box constraint...Show More

Stable and Lightweight Deep Primal-Dual Unrolling for Constrained Image Restoration with Convolutional Sparse Coding

Takafumi Ueki;Kazuki Naganuma;Shunsuke Ono

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Graph Learning Over Polytopic Uncertain Graph

Masako Kishida;Shunsuke Ono

IEEE Signal Processing Letters

Year: 2025 | Volume: 32 | Journal Article |

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This letter introduces a graph learning approach leveraging prior knowledge of graph topology. For this, we integrate the concept of polytopic uncertainty into existing approaches that learn graph Laplacians and adjacency matrices, constraining the solution space to a polytopic set. Our approach offers improved accuracy with reduced computational cost by focusing on a smaller solution space, effec...Show More

Graph Learning Over Polytopic Uncertain Graph

Masako Kishida;Shunsuke Ono

IEEE Signal Processing Letters

Year: 2025 | Volume: 32 | Journal Article |

Sparse Regularization With Reverse Sorted Sum of Squares via an Unrolled Difference-of-Convex Approach

Takayuki Sasaki;Kazuya Hayase;Masaki Kitahara;Shunsuke Ono

IEEE Open Journal of Signal Processing

Year: 2025 | Volume: 6 | Journal Article |

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This paper proposes a sparse regularization method with a novel sorted regularization function. Sparse regularization is commonly used to solve underdetermined inverse problems. Traditional sparse regularization functions, such as $L_{1}$-norm, suffer from problems like amplitude underestimation and vanishing perturbations. The reverse ordered weighted $L_{1}$-norm (ROWL) addresses these issues bu...Show More

Sparse Regularization With Reverse Sorted Sum of Squares via an Unrolled Difference-of-Convex Approach

Takayuki Sasaki;Kazuya Hayase;Masaki Kitahara;Shunsuke Ono

IEEE Open Journal of Signal Processing

Year: 2025 | Volume: 6 | Journal Article |

Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data

Eisuke Yamagata;Kazuki Naganuma;Shunsuke Ono

IEEE Transactions on Signal and Information Processing over Networks

Year: 2025 | Volume: 11 | Journal Article |

Cited by: Papers (1)

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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 conventional methods for time-varying graph signal recovery have been proposed under the assumption that the underlying graph that houses the signals is static. However, in light of rapid advances in sensor technology, the as...Show More

Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data

Eisuke Yamagata;Kazuki Naganuma;Shunsuke Ono

IEEE Transactions on Signal and Information Processing over Networks

Year: 2025 | Volume: 11 | Journal Article |

Hyperspectral Unmixing With Row-Sparsity Enhancement: A Difference-of-Convex Approach

Kazuki Naganuma;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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This paper proposes a new hyperspectral unmixing method that enhances the row sparsity of an abundance matrix via a difference-of-convex (DC) approach. One promising unmixing technique is to promote the row sparsity by designing and solving optimization problems. However, existing methods are unable to both fully enhance the row sparsity and guarantee the convergence of their optimization algorith...Show More

Hyperspectral Unmixing With Row-Sparsity Enhancement: A Difference-of-Convex Approach

Kazuki Naganuma;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

Generalized Graph Signal Sampling under Subspace Priors by Difference-of-Convex Minimization

Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

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This paper proposes an effective approach for sampling graph signals under the subspace prior. Unlike conventional methods that assume bandlimited signals, our method, based on generalized sampling theory, designs a sampling operator suitable for general graph signals beyond bandlimitedness. We formulate a feasibility problem for designing the sampling operator for graph signals under the subspace...Show More

Generalized Graph Signal Sampling under Subspace Priors by Difference-of-Convex Minimization

Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

Rotation Invariant Spatio-Spectral Total Variation for Hyperspectral Image Denoising

Shingo Takemoto;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

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We propose a novel regularization function, named Rotation-Invariant Spatio-Spectral Total Variation (RISSTV), for hyperspectral (HS) image denoising. Spatio-Spectral Total Variation (SSTV) and its extended methods, defined using the second-order spatio-spectral differences, are known as popular regularization approaches that capture the HS image-specific spatio-spectral piecewise smoothness for e...Show More

Rotation Invariant Spatio-Spectral Total Variation for Hyperspectral Image Denoising

Shingo Takemoto;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

Sensor Placement Problem on Networks for Sensors with Multiple Specifications

Junya Hara;Shunsuke Ono;Hiroshi Higashi;Yuichi Tanaka

2024 32nd European Signal Processing Conference (EUSIPCO)

Year: 2024 | Conference Paper |

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In this paper, we address a sensor placement problem on networks for a set of sensors with multiple specifications. Sensor placement problem has been studied extensively in environmental monitoring applications on various sensor networks, such as water supply, power electric, and gas pipeline networks. While most existing studies assume all sensors have the same specifications, one may need to con...Show More

Sensor Placement Problem on Networks for Sensors with Multiple Specifications

Junya Hara;Shunsuke Ono;Hiroshi Higashi;Yuichi Tanaka

2024 32nd European Signal Processing Conference (EUSIPCO)

Year: 2024 | Conference Paper |

Fine-Scaled Predictive Modeling of Road Surface Conditions and Temperature in Urban Areas

Keita Ishii;Shunsuke Ono;Takeshi Masago;Masamu Ishizuki;Teppei Mori;Yasushi Hanatsuka

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 11 | Journal Article |

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Road administrators require fine-scaled information regarding road surface conditions to ensure efficient operation during winter periods. However, conventional models offer low-resolution information at a scale comparable to meteorological meshes or the spatial configuration of road weather information systems. Additionally, few methods have been proposed for predicting road surface conditions sp...Show More

Fine-Scaled Predictive Modeling of Road Surface Conditions and Temperature in Urban Areas

Keita Ishii;Shunsuke Ono;Takeshi Masago;Masamu Ishizuki;Teppei Mori;Yasushi Hanatsuka

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 11 | Journal Article |

Sparse Index Tracking: Simultaneous Asset Selection and Capital Allocation via ℓ0 -Constrained Portfolio

Eisuke Yamagata;Shunsuke Ono

IEEE Open Journal of Signal Processing

Year: 2024 | Volume: 5 | Journal Article |

Cited by: Papers (1)

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Sparse index tracking is a prominent passive portfolio management strategy that constructs a sparse portfolio to track a financial index. A sparse portfolio is preferable to a full portfolio in terms of reducing transaction costs and avoiding illiquid assets. To achieve portfolio sparsity, conventional studies have utilized $\ell _{p}$-norm regularizations as a continuous surrogate of the $\ell _{...Show More

Sparse Index Tracking: Simultaneous Asset Selection and Capital Allocation via ℓ0 -Constrained Portfolio

Eisuke Yamagata;Shunsuke Ono

IEEE Open Journal of Signal Processing

Year: 2024 | Volume: 5 | Journal Article |

Robust Spatiotemporal Fusion of Satellite Images: A Constrained Convex Optimization Approach

Ryosuke Isono;Kazuki Naganuma;Shunsuke Ono

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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This article proposes a novel spatiotemporal (ST) fusion framework for satellite images, named robust optimization-based ST fusion (ROSTF). ST fusion is a promising approach to resolve a tradeoff between the temporal and spatial resolution of satellite images. Although many ST fusion methods have been proposed, most of them are not designed to explicitly account for noise in observed images, despi...Show More

Robust Spatiotemporal Fusion of Satellite Images: A Constrained Convex Optimization Approach

Ryosuke Isono;Kazuki Naganuma;Shunsuke Ono

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Toward Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization

Kazuki Naganuma;Shunsuke Ono

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2024 | Volume: 17 | Journal Article |

Cited by: Papers (3)

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Hyperspectral (HS) unmixing is the process of decomposing an HS image into material-specific spectra (endmembers) and their spatial distributions (abundance maps). Existing unmixing methods have two limitations with respect to noise robustness. First, if the input HS image is highly noisy, even if the balance between sparse and piecewise-smooth regularizations for abundance maps is carefully adjus...Show More

Toward Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization

Kazuki Naganuma;Shunsuke Ono

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2024 | Volume: 17 | Journal Article |

Enhancing Hyperspectral Anomaly Detection by Difference-of-Convex Sparse Anomaly Modeling

Koyo Sato;Kazuki Naganuma;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Cited by: Papers (3)

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We propose a hyperspectral (HS) anomaly detection method using a novel characterization of anomalies. Among HS anomaly detection approaches, decomposition-based methods, which simultaneously estimate a background part and an anomaly part from an HS image, have attracted much attention. In these methods, various approaches have been proposed for mathematical modeling of the background part, but the...Show More

Enhancing Hyperspectral Anomaly Detection by Difference-of-Convex Sparse Anomaly Modeling

Koyo Sato;Kazuki Naganuma;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Risk-Managed Sparse Index Tracking Via Market Graph Clustering

Eisuke Yamagata;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

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In this paper, we propose a risk-managed sparse index tracking framework. In this approach, we impose market-graph neutrality and turnover sparsity on the index tracking problem. Historically, sector neutrality has been researched to diversify investment across various sectors, preventing the portfolio from being biased toward specific industries. This strategy can act as a failsafe in scenarios w...Show More

Risk-Managed Sparse Index Tracking Via Market Graph Clustering

Eisuke Yamagata;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Temporally-Guided Total Variation For Robust Spatiotemporal Fusion Of Satellite Images

Ryosuke Isono;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

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This paper proposes a new regularization function specific to Spatiotemporal (ST) fusion, named temporally-guided total variation (TGTV). ST fusion is a promising approach to address a trade-off between the temporal and spatial resolution of satellite images. In general, satellite images are severely degraded by noise due to the observation instrument and environment. However, existing ST fusion m...Show More

Temporally-Guided Total Variation For Robust Spatiotemporal Fusion Of Satellite Images

Ryosuke Isono;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

A Convergent Primal-Dual Deep Plug-and-Play Algorithm for Constrained Image Restoration

Yodai Suzuki;Ryosuke Isono;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

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We propose a new deep plug-and-play (PnP) algorithm for constrained image restoration with guaranteed theoretical convergence. The PnP strategy, which incorporates off-the-shelf Gaussian denoisers into proximal splitting algorithms, has demonstrated outstanding performance in a variety of image restoration tasks. However, theoretically guaranteeing its convergence under reasonable assumptions is e...Show More

A Convergent Primal-Dual Deep Plug-and-Play Algorithm for Constrained Image Restoration

Yodai Suzuki;Ryosuke Isono;Shunsuke Ono

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

JPEG Artifact Removal for Hyperspectral Images Based on Spatial-Spectral Regularization

Ryunosuke Eguchi;Iori Kobayashi;Shunsuke Ono;Ryo Matsuoka

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2023 | Conference Paper |

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A hyperspectral image (HSI) contains vast data, storing hundreds of spectral information with higher resolution than an RGB image. Therefore, considering practical aspects such as data transmission and storage, it is essential to explore the application of standard JPEG compression to an HSI. Conventional JPEG artifact removal methods, such as block and mosquito noise, recover a JPEG image by impr...Show More

JPEG Artifact Removal for Hyperspectral Images Based on Spatial-Spectral Regularization

Ryunosuke Eguchi;Iori Kobayashi;Shunsuke Ono;Ryo Matsuoka

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2023 | Conference Paper |

Cross-Frame Foreground Structural Similarity Modeling by Convolutional Sparse Representation

Kazuki Naganuma;Shunsuke Ono

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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In this paper, we propose a foreground-background separation (FBS) method using a novel foreground model based on convolutional sparse representation (CSR). Existing FBS methods are based on the assumption that the foreground component is sparse or continuous in time, resulting in poor separation performance for videos containing sparse noise/fast moving objects or for low frame rate ones. Therefo...Show More

Cross-Frame Foreground Structural Similarity Modeling by Convolutional Sparse Representation

Kazuki Naganuma;Shunsuke Ono

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2023 | Conference Paper |

Epigraphically-Relaxed Linearly-Involved Generalized Moreau-Enhanced Model for Layered Mixed Norm Regularization

Akari Katsuma;Seisuke Kyochi;Shunsuke Ono;Ivan Selesnick

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

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This paper proposes an epigraphically-relaxed linearly-involved generalized Moreau-enhanced (ER-LiGME) model for layered mixed norm regularization. Group sparse and low-rank (GSpLr)-aware modeling using ℓ1 /nuclear-norm-based layered mixed norms has succeeded in precise high dimensional signal recovery, e.g., images and videos. Our previous work significantly expands the potential of the GSpLr-awa...Show More

Epigraphically-Relaxed Linearly-Involved Generalized Moreau-Enhanced Model for Layered Mixed Norm Regularization

Akari Katsuma;Seisuke Kyochi;Shunsuke Ono;Ivan Selesnick

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications

Kazuki Naganuma;Shunsuke Ono

IEEE Transactions on Signal Processing

Year: 2023 | Volume: 71 | Journal Article |

Cited by: Papers (9)

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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 optimization problems. To speed up the convergence of P-PDS, a design method has been proposed to automatically determine appropriate preconditioners from the problem structure. However, the existing method has two limita...Show More

Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications

Kazuki Naganuma;Shunsuke Ono

IEEE Transactions on Signal Processing

Year: 2023 | Volume: 71 | Journal Article |

Multi-Resolution Convolutional Dictionary Learning for Riverbed Dynamics Modeling

E. Kobayashi;H. Yasuda;K. Hayasaka;Y. Otake;S. Ono;S. Muramatsu

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

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This work proposes a novel formulation of convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) incorporating a deep learning framework. CSC-DMD is a high-dimensional data analysis method with a convolutional synthesis dictionary and applicable to analyze dynamics such as seismic motions and river flows. An authors’ previous work has shown the effectiveness of CSC-DMD for riverbed state ...Show More

Multi-Resolution Convolutional Dictionary Learning for Riverbed Dynamics Modeling

E. Kobayashi;H. Yasuda;K. Hayasaka;Y. Otake;S. Ono;S. Muramatsu

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Enhancing Spatio-Spectral Regularization by Structure Tensor Modeling for Hyperspectral Image Denoising

Shingo Takemoto;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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We propose a new regularization function, named Spatio-Spectral Structure Tensor Total Variation (S3TTV), for hyperspectral image (HSI) denoising. Spatio-Spectral Total Variation (SSTV), defined using spatio-spectral second-order differences, is widely known as a regularization function for HSI that can effectively remove noise while avoiding spatial over-smoothing. However, since SSTV only refers...Show More

Enhancing Spatio-Spectral Regularization by Structure Tensor Modeling for Hyperspectral Image Denoising

Shingo Takemoto;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Static-Scene Constrained Optimization for Matrix/Tensor-Decomposition-free Foreground-Background Separation

Kazuki Naganuma;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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We propose an efficient foreground-background separation (FBS) method for (possibly noisy) video data. Most existing FBS methods model the background as a low-rank component. However, this approach is computationally expensive because it requires matrix/tensor decomposition of high-dimensional videos. In this paper, we first introduce a new background model, named static scene constraint (SSC), to...Show More

Static-Scene Constrained Optimization for Matrix/Tensor-Decomposition-free Foreground-Background Separation

Kazuki Naganuma;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Robust Hyperspectral Anomaly Detection with Simultaneous Mixed Noise Removal via Constrained Convex Optimization

Koyo Sato;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Hyperspectral (HS) anomaly detection is the task of identifying pixels with spectral signatures that differ significantly from surrounding pixels. Most existing anomaly detection methods do not take into account the effect of noise in HS images, or if they do, it is only Gaussian noise. In practice, however, it is inevitable that non-Gaussian noise is superimposed on HS images due to sensor failur...Show More

Robust Hyperspectral Anomaly Detection with Simultaneous Mixed Noise Removal via Constrained Convex Optimization

Koyo Sato;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Robust Spatiotemporal Fusion of Satellite Images via Convex Optimization

Ryosuke Isono;Kazuki Naganuma;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Spatiotemporal fusion (ST fusion) is a feasible solution to resolve a tradeoff between the temporal and spatial resolutions of satellite images. Although many ST fusion methods have been proposed, most methods have not been developed that explicitly take noise in observed images into account, despite the inevitable influence of noise caused by the observation equipment and environment. In this pap...Show More

Robust Spatiotemporal Fusion of Satellite Images via Convex Optimization

Ryosuke Isono;Kazuki Naganuma;Shunsuke Ono

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

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