Hancheng Yu, Wenkai Wang, Wenshi Fan, Vol. 13, No. 2, February 27, 2019
10.3837/tiis.2019.02.020

• Motion deblurring
• blind deconvolution
• kernel estimation
• adaptive iterative strategy

Nguyen Manh Dung, Dongkeun Kim, Soonghwan Ro, Vol. 12, No. 12, December 30, 2018
10.3837/tiis.2018.12.022

• Smoke detection
• Deep Learning
• Convolutional Neural Network
• cascade classification
• BAIR reference CaffeNet

Jinhua Lin, Yu Yao, Yanjie Wang, Vol. 12, No. 11, November 29, 2018
10.3837/tiis.2018.11.021

• instance segmentation
• detector
• fully convolution network
• amodal proposals

Sheraz Naseer, Yasir Saleem, Vol. 12, No. 10, October 30, 2018
10.3837/tiis.2018.10.028

• Network intrusion detection
• Deep Convolutional Neural Networks
• Deep Learning
• CNN
• IDS
• information security

Muhammad Khawar Bashir, Yasir Saleem, Vol. 12, No. 8, August 30, 2018
10.3837/tiis.2018.08.013

• Semi-supervised deep hashing
• binary hashing
• convolutional auto-encoders
• Deep Learning
• image semantics

Minkyu Lim, Donghyun Lee, Hosung Park, Yoseb Kang, Junseok Oh, Jeong-Sik Park, Gil-Jin Jang, Ji-Hwan Kim, Vol. 12, No. 6, June 29, 2018
10.3837/tiis.2018.06.017

• Audio event classification
• Convolutional neural networks
• Deep Learning

Ning Wang, Yanjun Peng, Yuanhong Wang, Meiling Wang, Vol. 12, No. 6, June 29, 2018
10.3837/tiis.2018.06.021

• Skin lesion
• Image Segmentation
• Convolutional neural networks
• adversarial network.

Kaiping Xu, Zheng Qin, Guolong Wang, Huidi Zhang, Kai Huang, Shuxiong Ye, Vol. 12, No. 5, May 30, 2018
10.3837/tiis.2018.05.019

• Multi-focus image fusion
• fully convolutional neural network
• deconvolution decoder
• convolution fusion

Vladimir Tyan1, Doohyun Kim, Vol. 12, No. 2, February 27, 2018
10.3837/tiis.2018.02.009

• computer vision
• object tracking
• Convolutional Neural Network
• Particle Filter
• GPU

Zheheng Rao, Chunyan Zeng, Minghu Wu, Zhifeng Wang, Nan Zhao, Min Liu Xiangkui Wan, Vol. 12, No. 1, January 30, 2018
10.3837/tiis.2018.01.020

• unsupervised priori algorithm
• “high-capacity” convolution layers
• residual network
• multi-level quick link
• Dropout layer