Disparity-based space-variant image deblurring

2013 ◽  
Vol 28 (7) ◽  
pp. 792-808 ◽  
Author(s):  
Changsoo Je ◽  
Hyeon Sang Jeon ◽  
Chang-Hwan Son ◽  
Hyung-Min Park
Keyword(s):  
Image Deblurring ◽  
Space Variant

scholarly journals Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring

2018 ◽  
Vol 26 (11) ◽  
pp. 14375 ◽  
Author(s):  
Yemeng Chen ◽  
Mengmeng Chen ◽  
Li Zhu ◽  
Jane Y. Wu ◽  
Sidan Du ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Deblurring ◽  
Microscopy Image ◽  
Space Variant

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

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.

Sign in / Sign up

Export Citation Format

Share Document