Fast-time consecutive confocal image deblurring using spatiotemporal fused regularization

2019 ◽  
Vol 58 (19) ◽  
pp. 5148
Author(s):  
Tao He ◽  
Yasheng Sun ◽  
Jin Qi ◽  
Haiqing Huang ◽  
Jie Hu
Keyword(s):  
Image Deblurring ◽  
Fast Time ◽  
Confocal Image

Fragmental weight-conservation combining scheme for statistical signal transmissions under fast time-varying channels

2020 ◽  
Vol 17 (1) ◽  
pp. 118-128
Author(s):  
Xingwei Wang ◽  
Ting Zhou ◽  
Tianheng Xu ◽  
Songlin Feng ◽  
Honglin Hu ◽  
...  
Keyword(s):  
Fast Time ◽  
Time Varying ◽  
Time Varying Channels

Analysis of Airport Surface Schedulers Using Fast-time Simulation

2013 ◽  
Author(s):  
Justin V. Montoya ◽  
Robert D. Windhorst ◽  
Steve Stroiney ◽  
Katy Griffin ◽  
Aditya Saraf ◽  
...  
Keyword(s):  
Fast Time ◽  
Time Simulation

Sparsity and Smoothing Multi-Regularization Constraints for Blind Image Deblurring

2013 ◽  
Vol 24 (5) ◽  
pp. 1143-1154 ◽  
Author(s):  
Shu TANG ◽  
Wei-Guo GONG ◽  
Jian-Hua ZHONG
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

Fast Time Domain Integral Equation Solvers for Large-Scale Electromagnetic Analysis

2004 ◽  
Author(s):  
Eric Michielsssen ◽  
Weng C. Chew ◽  
Jianming Jin ◽  
Balasubramaniam Shanker
Keyword(s):  
Integral Equation ◽  
Time Domain ◽  
Large Scale ◽  
Electromagnetic Analysis ◽  
Fast Time ◽  
Domain Integral ◽  
Time Domain Integral Equation

scholarly journals A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5312
Author(s):  
Yanni Zhang ◽  
Yiming Liu ◽  
Qiang Li ◽  
Jianzhong Wang ◽  
Miao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Image Features ◽  
Image Feature ◽  
Model Complexity ◽  
Small Scale ◽  
Feature Maps ◽  
Learning Framework ◽  
Channel Information ◽  
Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

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