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Stable and Lightweight Deep Primal-Dual Unrolling for Constrained Image Restoration with Convolutional Sparse Coding
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
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
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
Sparse Regularization With Reverse Sorted Sum of Squares via an Unrolled Difference-of-Convex Approach
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
Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data
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
Hyperspectral Unmixing With Row-Sparsity Enhancement: A Difference-of-Convex Approach
2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
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
Generalized Graph Signal Sampling under Subspace Priors by Difference-of-Convex Minimization
2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
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
Rotation Invariant Spatio-Spectral Total Variation for Hyperspectral Image Denoising
2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Year: 2024 | Conference Paper |
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
Sensor Placement Problem on Networks for Sensors with Multiple Specifications
2024 32nd European Signal Processing Conference (EUSIPCO)
Year: 2024 | Conference Paper |
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
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
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
Sparse Index Tracking: Simultaneous Asset Selection and Capital Allocation via ℓ0 -Constrained Portfolio
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
Robust Spatiotemporal Fusion of Satellite Images: A Constrained Convex Optimization Approach
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
Toward Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain Regularization
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
Enhancing Hyperspectral Anomaly Detection by Difference-of-Convex Sparse Anomaly Modeling
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
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
Risk-Managed Sparse Index Tracking Via Market Graph Clustering
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
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
Temporally-Guided Total Variation For Robust Spatiotemporal Fusion Of Satellite Images
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
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
A Convergent Primal-Dual Deep Plug-and-Play Algorithm for Constrained Image Restoration
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
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
JPEG Artifact Removal for Hyperspectral Images Based on Spatial-Spectral Regularization
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Year: 2023 | Conference Paper |
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
Cross-Frame Foreground Structural Similarity Modeling by Convolutional Sparse Representation
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
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
Epigraphically-Relaxed Linearly-Involved Generalized Moreau-Enhanced Model for Layered Mixed Norm Regularization
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
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
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications
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
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 |
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
Enhancing Spatio-Spectral Regularization by Structure Tensor Modeling for Hyperspectral Image Denoising
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
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
Static-Scene Constrained Optimization for Matrix/Tensor-Decomposition-free Foreground-Background Separation
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
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
Robust Hyperspectral Anomaly Detection with Simultaneous Mixed Noise Removal via Constrained Convex Optimization
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
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 Spatiotemporal Fusion of Satellite Images via Convex Optimization
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
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
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