A simulation study on the choice of regularization parameter in ℓ2-norm ultrasound image restoration

2015 ◽  
Author(s):  
Zhouye Chen ◽  
Adrian Basarab ◽  
Denis Kouame
Keyword(s):  
Image Restoration ◽  
Simulation Study ◽  
Regularization Parameter ◽  
Ultrasound Image

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.

scholarly journals Iterative choice of the optimal regularization parameter in TV image restoration

2015 ◽  
Vol 9 (4) ◽  
pp. 1171-1191 ◽  
Author(s):  
Alina Toma ◽  
◽  
Bruno Sixou ◽  
Françoise Peyrin
Keyword(s):  
Image Restoration ◽  
Regularization Parameter

Selecting Regularization Parameter In Image Restoration Based On the Variance Of Noise

2009 ◽  
Vol 29 (9) ◽  
pp. 2395-2401
Author(s):  
刘鹏 Liu Peng ◽  
刘定生 Liu Dingsheng
Keyword(s):  
Image Restoration ◽  
Regularization Parameter

Adaptive Box-Constrained Total Variation Image Restoration Using Iterative Regularization Parameter Adjustment Method

2015 ◽  
Vol 29 (07) ◽  
pp. 1554003 ◽  
Author(s):  
Zhining Zhu ◽  
Guangcheng Cai ◽  
You-Wei Wen
Keyword(s):  
Image Restoration ◽  
Regularization Parameter ◽  
Iterative Solution ◽  
Original Image ◽  
Trial And Error ◽  
Box Constraints ◽  
Iterative Regularization ◽  
Trial And Error Method ◽  
Restoration Problem ◽  
Error Method

In this paper, we consider the problem of image restoration with box-constraints. Image restoration problem is ill-conditioned and the regularization approach has widely been used to stabilize the solution. The restored image highly depends on the choice of the regularization parameter. The regularization parameter is generally determined by trial-and-error method when no true original image is available. Obviously, it is time consuming. The main aim in this paper is to develop an algorithm to choose the regularization parameter automatically when the box-constraints are imposed. In the proposed algorithm, the regularization parameter is adaptively determined by the previous iterative solution. Numerical simulations are used to demonstrate the performance of the proposed method.

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