Presentation schedule after Sep. 2021

Internatinal Conference

  • Gai YAMAMOTO, Yuya KODAMA, Shogo MURAMATSU, Samuel CHOI, Gwanggil JEON, “ACCELERATION OF PDS–BASED HIGH–DIMENSIONAL SIGNAL RESTORATION,” APSIPA ASC 2021, Tokyo, Dec. 2021
  • Ruiki KOBAYASHI, Shogo MURAMATSU, Shunsuke ONO, “PROXIMAL GRADIENT-BASED LOOP UNROLLING WITH INTERSCALE THRESHOLDING,” APSIPA ASC 2021, Tokyo, Dec. 2021
  • Izbaila IMTIAZ, Imran AHMED, Gwanggil JEON, Shogo MURAMATSU, “AN EFFICIENT IMAGE PROCESSING AND MACHINE LEARNING BASED TECHNIQUE FOR SKIN LESION SEGMENTATION AND CLASSIFICATION,”   APSIPA ASC 2021, Tokyo, Dec. 2021

Presentation schedule after June 2021

International Conference

  • Yusuke Arai, Shogo Muramatsu, Hiroyasu Yasuda, Kiyoshi Hayasaka, Yu Otake: Sparse-Coded Dynamic Mode Decomposition on Graph for Prediction of River Water Level DistributionProc. of 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , June 2021, to appear
  • M Ibnul Morshed and Shogo Muramatsu: Improvement of Object Detection from SAR Image Using Speckle Filter, Proc. of ITC-CSCC2021, June 2021, to appear
  • Jikai Li, Ruiki Kobayashi, Shogo Muramatsu and Gwanggil Jeon: Image Restoration with Structured Deep Image Prior, Proc. of ITC-CSCC2021, June 2021, to appear
  • Dongqi Liu, Yutaka Naito, Chen Zhang, Shogo Muramatsu, Hiroyasu Yasuda, Kiyoshi Hayasaka and Yu Otake: River Flow Path Control with Reinforcement Learning, Proc. of 2021 IEEE International Conference on Autonomous Systems (ICAS), Aug. 2021, to appear




Published SaivDr-Release20200903

We updated SaivDr (Sparsity-Aware Image and Volumetric Data Restoration) package for the first time in about six months.

New this time, we added custom layers and sample codes for use with MATLAB Deep Learning Toolbox. It allows for more flexible DAG configuration than before.

NSOLT enables you to realize Parseval tight, symmetric and multi-resolution convolutional layers, and you can place NSOLT as a convolutional layer in a corner of a convolutional neural network.

We hope you will give it a try.

Acknowledgments: This work was supported by KAKENHI JP19H04135.