scholarly journals Iteratively Reweighted Blind Deconvolution With Adaptive Regularization Parameter Estimation

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 11959-11973 ◽  
Author(s):  
Houzhang Fang ◽  
Yi Chang ◽  
Gang Zhou ◽  
Lizhen Deng
Keyword(s):  
Parameter Estimation ◽  
Blind Deconvolution ◽  
Regularization Parameter ◽  
Adaptive Regularization

An Adaptive Variational PDE Based Image Restoration Model Using Hopfield Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 174-178
Author(s):  
Wei Sun ◽  
Sheng Nan Liu
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Adaptive Regularization ◽  
Proposed Model ◽  
Degraded Image ◽  
Restoration Problem ◽  
Restoration Model

An adaptive variational partial differential equation (PDE) based aproach for restoration of gray level images degraded by a known shift-invariant blur function and additive noise is presented. The restoration problem of a degraded image is solved by minimizing this model, and this minimizing problem is realized by using Hopfield neural network. In the proposed image restoration model, an adaptive regularization parameter is developed instead of the constant regularization parameter used in previous PDE model. The value of the adaptive regularization parameter changes according to different regions of the image to remove noises and preserve edge better. Several computer simulation results show that the image restoration results of the proposed model both look better and have better SNR (Signal to Noise Ratio) than the previous variational PDE based model.

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