Data are said to follow the transform (or analysis) sparsity model if they become sparse
when acted on by a linear operator called a sparsifying transform. Several algorithms have …

We propose a data-driven design method of perfect-reconstruction filterbank (PRFB) for
sound-source enhancement (SSE) based on deep neural network (DNN). DNNs have been …

This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD)
by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse …

This paper proposes a novel learnable linear transform, locallystructured unitary network
(LSUN), that captures tangent spaces of a manifold latent in high-dimensional data …

This study proposes a novel approach for the analysis of high-dimensional time series data
utilizing a linear shift-variant transform. Central to the methodology is the Locally-Structured …

This work proposes a novel image restoration technique inspired by the Ulyanov's deep
image prior (DIP) method. DIP uses a deep convolutional network as an image prior to …

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 …

This study extends a self-supervised image denoising technique proposed by the authors to
a more general image restoration method. The previous work was inspired by Ulyanov's …

In this study, a novel image restoration method is proposed by introducing a structured
convolutional neural network (CNN) in the deep image prior (DIP) framework. CNN has …

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 …