Search Results for author: Tatsuya Yokota

Found 13 papers, 2 papers with code

Adaptive Block Sparse Regularization under Arbitrary Linear Transform

no code implementations27 Jan 2024 Takanobu Furuhashi, Hidekata Hontani, Tatsuya Yokota

We propose a convex and fast signal reconstruction method for block sparsity under arbitrary linear transform with unknown block structure.

ADMM-MM Algorithm for General Tensor Decomposition

no code implementations19 Dec 2023 Manabu Mukai, Hidekata Hontani, Tatsuya Yokota

In this paper, we propose a new unified optimization algorithm for general tensor decomposition which is formulated as an inverse problem for low-rank tensors in the general linear observation models.

Tensor Decomposition

Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space

no code implementations18 Mar 2022 Tatsuya Yokota

Based on the model under inverse delay-embedding, we propose to constrain the matrix to be rank-1 with smooth factor vectors.

Audio inpainting Time Series +1

Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches

no code implementations10 Mar 2022 Tatsuya Yokota, Hidekata Hontani

This study proposes a framework for manifold learning of image patches using the concept of equivalence classes: manifold modeling in quotient space (MMQS).

Deblurring Denoising +3

Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space

no code implementations CVPR 2022 Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota

Tensor completion using multiway delay-embedding transform (MDT) (or Hankelization) suffers from the large memory requirement and high computational cost in spite of its high potentiality for the image modeling.

Dynamic PET Image Reconstruction Using Nonnegative Matrix Factorization Incorporated With Deep Image Prior

no code implementations ICCV 2019 Tatsuya Yokota, Kazuya Kawai, Muneyuki Sakata, Yuichi Kimura, Hidekata Hontani

Experimental results show that the proposed method outperforms conventional methods and can extract spatial factors that represent the homogeneous tissues.

Image Reconstruction

Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"

no code implementations25 Sep 2019 Tatsuya Yokota, Hidekata Hontani, Qibin Zhao, Andrzej Cichocki

The proposed approach is dividing the convolution into ``delay-embedding'' and ``transformation (\ie encoder-decoder)'', and proposing a simple, but essential, image/tensor modeling method which is closely related to dynamical systems and self-similarity.

Denoising Image Reconstruction +2

Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior

1 code implementation8 Aug 2019 Tatsuya Yokota, Hidekata Hontani, Qibin Zhao, Andrzej Cichocki

The proposed approach is dividing the convolution into ``delay-embedding'' and ``transformation (\ie encoder-decoder)'', and proposing a simple, but essential, image/tensor modeling method which is closely related to dynamical systems and self-similarity.

Denoising Image Reconstruction +2

Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization

no code implementations10 Jan 2018 Tatsuya Yokota, Hidekata Hontani

In the sense of trade-off tuning, the noisy tensor completion problem with the `noise inequality constraint' is better choice than the `regularization' because the good noise threshold can be easily bounded with noise standard deviation.

Denoising Missing Elements +1

Smooth PARAFAC Decomposition for Tensor Completion

no code implementations25 May 2015 Tatsuya Yokota, Qibin Zhao, Andrzej Cichocki

The proposed method admits significant advantages, owing to the integration of smooth PARAFAC decomposition for incomplete tensors and the efficient selection of models in order to minimize the tensor rank.

Matrix Completion

Cannot find the paper you are looking for? You can Submit a new open access paper.