Blind Deconvolution with Sparse Priors on the Deconvolution Filters

2006 ◽  
pp. 658-665 ◽  
Author(s):  
Hyung-Min Park ◽  
Jong-Hwan Lee ◽  
Sang-Hoon Oh ◽  
Soo-Young Lee
Keyword(s):  
Blind Deconvolution ◽  
Sparse Priors

Multi-frame blind deconvolution using sparse priors

2012 ◽  
Vol 285 (9) ◽  
pp. 2276-2288 ◽  
Author(s):  
Wende Dong ◽  
Huajun Feng ◽  
Zhihai Xu ◽  
Qi Li
Keyword(s):  
Blind Deconvolution ◽  
Sparse Priors

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Blind Deconvolution to Improve Classification of Transient Source Signals in Multipath

2000 ◽  
Author(s):  
Lisa A. Pflug ◽  
George B. Smith ◽  
Michael K. Broadhead
Keyword(s):  
Blind Deconvolution ◽  
Transient Source ◽  
Source Signals

Convolutional plug-and-play sparse optimization for impulsive blind deconvolution

2021 ◽  
Vol 161 ◽  
pp. 107877
Author(s):  
Zhaohui Du ◽  
Han Zhang ◽  
Xuefeng Chen ◽  
Yixin Yang
Keyword(s):  
Blind Deconvolution ◽  
Sparse Optimization ◽  
Plug And Play
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