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 Interactive Entertainment Image Restoration Method Based on Hopfield Neural Network

2018 ◽  
Vol 232 ◽  
pp. 01008
Author(s):  
Shuangqing lv
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Hopfield Neural Network ◽  
Original Data ◽  
Small Scale ◽  
Interactive Entertainment ◽  
Degraded Image ◽  
Restoration Method ◽  
Traditional Image ◽  
Better Than

The traditional image restoration methods of interactive entertainment are based on the original data. This paper proposes an interactive entertainment image restoration method based on Hopfield neural network. Firstly, the nonlinear mapping relationship between the degraded image and the real image is preliminarily established through the network, and then optimized by the algorithm. Finally, the image restoration can be achieved through the network. The experiments show that it has higher feasibility and the recovery effect on small-scale blur is better than the existing method.

Hopfield neural network-based image restoration with adaptive mixed-norm regularization

2009 ◽  
Vol 7 (8) ◽  
pp. 686-689 ◽  
Author(s):  
许元男 Yuannan Xu ◽  
刘丽萍 Liping Liu ◽  
赵远 Yuan Zhao ◽  
靳辰飞 Chenfei Jin ◽  
孙秀冬 Xiudong Sun
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Hopfield Neural Network ◽  
Mixed Norm

scholarly journals Contribution to image restoration using a neural network model

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Karim Achour ◽  
Nadia Zenati ◽  
Oualid Djekoune
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Optimization Method ◽  
Nous Proposons ◽  
International Audience ◽  
High Level

International audience The reduction of the blur and the noise is an important task in image processing. Indeed, these two types of degradation are some undesirable components during some high level treatments. In this paper, we propose an optimization method based on neural network model for the regularized image restoration. We used in this application a modified Hopfield neural network. We propose two algorithms using the modified Hopfield neural network with two updating modes : the algorithm with a sequential updates and the algorithm with the n-simultaneous updates. The quality of the obtained result attests the efficiency of the proposed method when applied on several images degraded with blur and noise. La réduction du bruit et du flou est une tâche très importante en traitement d'images. En effet, ces deux types de dégradations sont des composantes indésirables lors des traitements de haut niveau. Dans cet article, nous proposons une méthode d'optimisation basée sur les réseaux de neurones pour résoudre le problème de restauration d'images floues-bruitées. Le réseau de neurones utilisé est le réseau de « Hopfield ». Nous proposons deux algorithmes utilisant deux modes de mise à jour: Un algorithme avec un mode de mise à jour séquentiel et un algorithme avec un mode de mise à jour n-simultanée. L'efficacité de la méthode mise en œuvre a été testée sur divers types d'images dégradées.

scholarly journals Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fayadh Alenezi ◽  
K. C. Santosh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Network Stability ◽  
Signal To Noise ◽  
Convergence Problem ◽  
Information Fidelity ◽  
Medical Image Enhancement

One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

scholarly journals Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 596 ◽  
Author(s):  
Nur Ezlin Zamri ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Alyaa Alway ◽  
Siti Zulaikha Mohd Jamaludin ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Science ◽  
Clonal Selection ◽  
Data Extraction ◽  
Hopfield Neural Network ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Proposed Model ◽  
Artificial Neural Network Ann ◽  
Logical Representation

Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

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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