Ensemble Steganalysis Based on Deep Residual Network

2020 ◽  
pp. 84-95
Author(s):  
Qiangjie Li ◽  
Guorui Feng ◽  
Hanzhou Wu ◽  
Xinpeng Zhang
Keyword(s):  
Residual Network

Deep fractal residual network for fast and accurate single image super resolution

2020 ◽  
Vol 398 ◽  
pp. 389-398 ◽  
Author(s):  
Yanghao Zhou ◽  
Jianfeng Dong ◽  
Yubin Yang
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Residual Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals A SYMMETRIC FULLY CONVOLUTIONAL RESIDUAL NETWORK WITH DCRF FOR ACCURATE TOOTH SEGMENTATION

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Yunbo Rao ◽  
Yilin Wang ◽  
Fanman Meng ◽  
Jiansu Pu ◽  
Jihong Sun ◽  
...  
Keyword(s):  
Residual Network ◽  
Tooth Segmentation

Star-Net: Spatial-Temporal Attention Residual Network for Video Deraining

2021 ◽  
Author(s):  
Wei Zhong ◽  
Xuefeng Zhang ◽  
Long Ma ◽  
Risheng Liu ◽  
Xin Fan ◽  
...  
Keyword(s):  
Temporal Attention ◽  
Residual Network

EffKannadaRes-NeXt: An efficient residual network for Kannada numeral recognition

2021 ◽  
Author(s):  
Aradhya Saini ◽  
Sandeep Daniel ◽  
Satyam Saini ◽  
Ankush Mittal
Keyword(s):  
Residual Network ◽  
Numeral Recognition

scholarly journals A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 546
Author(s):  
Zhenni Li ◽  
Haoyi Sun ◽  
Yuliang Gao ◽  
Jiao Wang
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Depth Map ◽  
Acquisition System ◽  
Depth Image ◽  
Fpga Design ◽  
Depth Sensing ◽  
Residual Network ◽  
Real Time Processing ◽  
Depth Maps

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

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