Multi-channel image deblurring using coded flashes

2021 ◽  
Author(s):  
Jaelin Lee ◽  
Byeungwoo Jeon
Keyword(s):  
Image Deblurring ◽  
Channel Image

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.

Blind image deblurring using group sparse representation

2020 ◽  
Vol 102 ◽  
pp. 102736 ◽  
Author(s):  
Zhenhua Xu ◽  
Huasong Chen ◽  
Zhenhua Li
Keyword(s):  
Sparse Representation ◽  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

Image Deblurring using Bi-directional Akamatsu Transform

2019 ◽  
Author(s):  
Candra Irawan ◽  
Jihad Maulana Akbar ◽  
De Rosal Ignatius Moses Setiadi ◽  
Eko Hari Rachmawanto ◽  
Christy Atika Sari ◽  
...  
Keyword(s):  
Image Deblurring

scholarly journals Restoration and Enhancement of Underwater Images Based on Bright Channel Prior

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yakun Gao ◽  
Haibin Li ◽  
Shuhuan Wen
Keyword(s):  
Histogram Equalization ◽  
New Method ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Underwater Environment ◽  
Dark Channel ◽  
Bright Channel Prior ◽  
Channel Image ◽  
Atmospheric Light

This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively.

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