TV: A Sparse Optimization Method for Impulse Noise Image Restoration

Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

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A Kronecker approximation with a convex constrained optimization method for blind image restoration

Optimization Letters ◽

10.1007/s11590-011-0370-7 ◽

2011 ◽

Vol 6 (7) ◽

pp. 1251-1264 ◽

Cited By ~ 8

Author(s):

A. Bouhamidi ◽

K. Jbilou

Keyword(s):

Constrained Optimization ◽

Image Restoration ◽

Optimization Method ◽

Blind Image Restoration ◽

Blind Image

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Nonsmooth Optimization Method for Image Restoration

Volume 3C: 15th Biennial Conference on Mechanical Vibration and Noise — Vibration Control, Analysis, and Identification ◽

10.1115/detc1995-0673 ◽

1995 ◽

Author(s):

Kazufumi Ito ◽

Karl Kunisch

Keyword(s):

Image Restoration ◽

Nonsmooth Optimization ◽

Numerical Optimization ◽

Augmented Lagrangian ◽

Energy Criterion ◽

Optimization Methods ◽

Optimization Method ◽

Augmented Lagrangian Methods ◽

Active Set ◽

First Order

Abstract In this paper we discuss applications of the numerical optimization methods for nonsmooth optimization, developed in [IK1] for the variational formulation of image restoration problems involving bounded variation type energy criterion. The Uzawa’s algorithm, first order augmented Lagrangian methods and Newton-like update using the active set strategy are described.

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Contribution to image restoration using a neural network model

Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées ◽

10.46298/arima.1829 ◽

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.

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