29 accepted papers
This paper proposes a method for sampling graph signals by designing a flexible sampling operator via a difference-of-convex (DC) based algorithm. Departing from conventional methods limited to bandlimited signals, our method extend the generalized sampling theory to handle graph signals beyond band…
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. Specifica…
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 rest…
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 meth…
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 portfo…
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 severel…
We propose a new regularization function, named Spatio-Spectral Structure Tensor Total Variation (S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> TTV), for hyperspectral image (HSI) denoising. Spatio-Spectral Total Variation (SSTV), defined us…
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 a…
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,…
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, despit…
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 video…
We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph s…
This paper proposes an effective and efficient destriping method for remote-sensing data. Destriping of remote-sensing data is an essential task because stripe noise not only degrades the visual quality but also seriously affects subsequent processing. We formulate the destriping problem as a convex…
This paper proposes an epigraphical reformulation (ER) technique for non-proximable mixed norm regularization. Various regularization methods using mixed norms have been proposed, where their optimization relies on efficient computation of the proximity operator of the mixed norms. Although the soph…
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 data-driven method of analysis used to describe a nonlinear dynamical system with a linear time-evolution equation. Com…
This paper proposes a randomized optimization framework for constrained signal reconstruction, where the word "constrained" implies that data-fidelity is imposed as a hard constraint instead of adding a data-fidelity term to an objective function to be minimized. Such formulation facilitates the sel…
A hyperspectral (HS) image has high spectral resolution information but low spatial resolution information. To get an HS image of high spatial and spectral resolution (high-spatial HS image), fusion techniques are actively studied, which synthesize an HS image of low spatial and high spectral resolu…
This paper proposes multiscale structure-tensor total variation (MSTV) for image recovery. Gradient vectors in local patches usually have similar directions, and thus each local gradient matrix (the set of gradient vectors) tends to be low rank. STV introduces this property by calculating the sum of…
This paper proposes color affine subspace pursuit (CASSP) for color artifact removal. Local patches in natural color images tend to exhibit a line distribution, so-called a color line. According to this characteristic, a convex-optimization-based image recovery with a local color nuclear norm (LCNN)…
Tensor factorization with hard and/or soft constraints has played an important role in signal processing and data analysis. However, existing algorithms for constrained tensor factorization have two drawbacks: (i) they require matrix-inversion; and (ii) they cannot (or at least is very difficult to)…
This work proposes a volumetric data restoration method, especially for data acquired through an optical coherence tomography (OCT) device. OCT is a technique for acquiring a tomographic image of a specimen object in a few μm scale by using a near infrared laser. The authors have been trying dynamic…
Acquiring high-resolution hyperspectral (HS) images is a very challenging task. To this end, hyperspectral pansharpening techniques have been widely studied, which estimate an HS image of high spatial and spectral resolution (high HS image) from a pair of an HS image of high spectral resolution but…
The hybrid steepest descent method (HSDM) [Yamada, '01] was introduced as a low-computational complexity tool for solving convex variational-inequality problems over the fixed-point set of non-expansive mappings in Hilbert spaces. Motivated by results on decentralized optimization, this study introd…
We propose an edge-preserving filtering method with a novel use of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient. Our method, termed as the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/199…
We propose a new regularization technique, named Hybrid Spatio-Spectral Total Variation (HSSTV), for hyperspectral image (HSI) restoration. Popular regularization techniques for HSIs are total variation functions (TV), and there have been proposed a variety of TVs for HSI restoration. However, they…
We propose an image colorization method using fast soft-thresholding of singular values (singular value thresholding). An image colorization method with nuclear norm minimization (NNM) has been proposed and brings good results. NNM usually requires iterative application of singular value decompositi…
We propose an efficient image restoration framework based on stochastic optimization. Image restoration usually requires some iterative methods for solving optimization problems that characterize restored images, where the multiplication of the observation matrix Φ ϵ Rm × n and variables has to be c…
We propose a new regularization function, named as Arranged Structure tensor Total Variation (ASTV), for multichannel image restoration. Since the standard structure tensor is a matrix whose eigenvalues well encodes local neighborhood information of an image, there has been proposed vectorial total…
This paper proposes a second-order discrete total generalized variation (TGV) for arbitrary graph signals, which we call the graph TGV (G-TGV). The original TGV was introduced as a natural higher-order extension of the well-known total variation (TV) and is an effective prior for piecewise smooth si…