image restoration-112


Iterative methods for solving the dual formulation arising from image restoration
TF Chan, K Chen ,Electronic Transactions on Numerical , 2007 ,
Abstract. Many variational models for image denoising restoration are formulated in primal
variables that are directly linked to the solution to be restored. If the total variation related
semi-norm is used in the models, one consequence is that extra regularization is needed 

Vector-valued image restoration with applications to magnetic resonance velocity imaging
YHP Ng ,Journal of WSCG, 2003 ,
ABSTRACT The analysis of blood flow patterns and the interaction between salient
topological flow features and cardiovascular structure plays an important role in the study of
cardiovascular function. Flow velocity images acquired by Magnetic Resonance (MR) 

Digital image restoration using autoregressive time series type models
H Allende, J Galbiati ,Bulletin European Spatial , 1998 ,
Abstract. We consider an non-symmetric half plane autoregressive image, where the image
intensity of a point is a linear combination of the intensitites of the eight nearest points
located on one quadrant of the coordinate plane, plus a normal white noise innovations 

A forward-backward algorithm for image restoration with sparse representations
JC Pesquet ,Signal Processing with , 2005 ,
ABSTRACT A number of recent approaches to image deconvolution and denoising in
Hilbert spaces consist of minimizing the sum of a residual energy and of a function
promoting a sparse decomposition in an orthonormal basis. Using convexanalytical tools, 

Two-level MRF models for image restoration and segmentation
British Machine Vision Conference, 2004 ,
Abstract We present a new general Bayesian formulation for simultaneously restoring and
segmenting piecewise smooth images. This implies estimation of the associated parameters
of the classes within an image, the class label for each image pixel and the number of 

Blurred image restoration using the type of blur and blur parameters identification on the neural network
V Karnaukhov ,SPIE , 2002 ,
ABSTRACT As a rule, blur is a form of bandwidth reduction of an ideal image owing to the
imperfect image formation process. It can be caused by relative motion between the camera
and the original scene, or by an optical system that is out of focus. Today there are 

PDE-based image restoration, I: Anti-staircasing and anti-diffusion
K Joo ,preprint, 2003 ,
Abstract This article is concerned with simulation issues arising in the PDE-based image
restoration such as the total variation minimization (TVM) and its generalizations. In
particular, we study the issues of staircasing and excessive dissipation of TVM-like 

Application of multigrid techniques to image restoration problems
M Donatelli, S Serra-Capizzano ,Proceedings of , 2002 ,
ABSTRACT We briefly describe a multigrid strategy for unilevel and two-level linear systems
whose coefficient matrix An belongs either to the Toeplitz class or to the cosine algebra of
type III and such that An can be naturally associated, in the spectral sense, with a 

Smoothing nonlinear conjugate gradient method for image restoration using nonsmooth nonconvex minimization
X Chen ,SIAM J. Imaging Sciences, 2010 ,
Abstract Image restoration problems are often converted into large-scale, nonsmooth and
nonconvex optimization problems. Most existing minimization methods are not efficient for
solving such problems. It is well-known that nonlinear conjugate gradient methods are 

General adaptive neighborhood image restoration, enhancement and segmentation
J Debayle,     Image Analysis and Recognition, 2006 ,Springer
This paper aims to outline the General Adaptive Neighborhood Image Processing (GANIP)
approach [1–3], which has been recently introduced. An intensity image is represented with
a set of local neighborhoods defined for each point of the image to be studied. These so-

Measurement and application of an infrared image restoration filter to improve the accuracy of surface temperature measurements of cubes
ER Meinders, GMP Van Kempen   Experiments in , 1999 ,Springer
Abstract This paper presents the experimental investigation of the application of an image
restoration technique aimed at improving the accuracy of infrared surface temperature
measurements of cubes placed in a vertical channel flow. These cubes, used to determine 

P LIU ,International Journal of Computational Cognition (http:// , 2004 ,
Abstract. Recently fuzzy techniques are widely applied in the area of digital image
restoration. Main achievements related are presented by four parts, in which the filters based
on fuzzy rank selection, fuzzy weighted, fuzzy neural network (FNN) and soft-switching are 

Point spread functions and their applications to forensic image restoration
CY Wen ,Forensic Science Journal, 2002 ,
ABSTRACT In the forensic science field, there are many criminal image data including
fingerprints, scene photos and surveillance camera videotapes. The evidence may become
the key point to solve crimes. However, the evidence may be contaminated by the noise 

A duality-based splitting method for l-TV image restoration with automatic regularization parameter choice
C Clason   SIAM J. Sci. Comput. v32 i3, 2009 ,
A novel splitting method is presented for the l1-TV restoration of degraded images subject to
impulsive noise. The functional is split into an l2-TV denoising and an l1-l2 deblurring part.
The dual problem of the relaxed functional is smooth with convex constraints, and can be 

Variational image restoration and decomposition with curvelet shrinkage
L Jiang, X Feng ,Journal of Mathematical Imaging and Vision, 2008 ,Springer
Abstract The curvelet is more suitable for image processing than the wavelet and able to
represent smooth and edge parts of image with sparsity. Based on this, we present a new
model for image restoration and decomposition via curvelet shrinkage. The new model 

A cross-validation approach to image restoration and blur identification
SJ Reeves ,1990 ,
An image is acquired in order to obtain a two-dimensional (2-D) representation of a three-
dimensional (3-D) scene. Unfortunately, many images represent scenes in an unsatisfactory
manner. Because physical imaging systems are imperfect, and because the conditions 

Spatial resolution enhancement of an IR system by image restoration techniques
D Bougeard, JP Vermeulen , of Quantitative infrared , 1994 ,
Abstract The spatial resolution of infrared systems is one of the major limitations in the use of
IR thermography for temperature measurements, mainly in the case of small objects of size
[1). The use of digital image restoration techniques for spatial resolution enhancement is a 

Variational bayes with Gauss-Markov-Potts prior models for joint image restoration and segmentation
H Ayasso ,Int. Conf. on Computer , 2008 ,
Abstract: In this paper, we propose a family of non-homogeneous Gauss-Markov fields with
Potts region labels model for images to be used in a Bayesian estimation framework, in
order to jointly restore and segment images degraded by a known point spread function 

Automated regularization parameter selection in multi-scale total variation models for image restoration
Y Dong, M Hintermüller ,Journal of Mathematical , 2011 ,Springer
Abstract Multi-scale total variation models for image restoration are introduced. The models
utilize a spatially dependent regularization parameter in order to enhance image regions
containing details while still sufficiently smoothing homogeneous features. The fully 

Spatially adaptive image restoration using fuzzy punctual kriging
AM Mirza, A Chaudhry ,Journal of Computer Science and , 2007 ,Springer
Abstract We present a general formulation based on punctual kriging and fuzzy concepts for
image restoration in spatial domain. Gray-level images degraded with Gaussian white noise
have been considered. Based on the pixel local neighborhood, fuzzy logic has been 

Phase estimation using the bispectrum and its application to image restoration
MG Kang, KT Lay ,Opt. Eng, 1991 ,
Abstract. We propose algorithms for estimating the phase of a determin-istic signal using its
bispectrum. The bispectrum of a signal is the (discrete) Fourier transform of its triple
correlation. While second-order statistics (eg, correlation function, power spectrum, etc.) 

Image restoration using total variation and anisotropic diffusion equation
M Li ,Frontiers of Electrical and Electronic Engineering in , 2007 ,Springer
Abstract This paper proposes a new model for the image restoration which combines the
total variation minimization with the “pure” anisotropic diffusion equation of Alvarez and
Morel. According to the introduction of new diffusion term, this model can not only remove 

Parallel implementation for iterative image restoration algorithms on a parallel DSP machine
GB Adams, LH Jamieson ,The Journal of VLSI , 1993 ,Springer
Many low-level image processing algorithms which are posed as variational problems can
be numerically solved using local and iterative relaxation algorithms. Because of the
structure of these algorithms, processing time will decrease nearly linearly with the 

Noise estimation in a noise-adjusted principal component transformation and hyperspectralimage restoration
B Xu ,Canadian Journal of Remote Sensing, 2008 ,
Abstract. We apply a noise-adjusted principal component transformation (NAPCT) to an
Earth Observing 1 (EO-1) Hyperion image whose noise structure is typically unknown. In this
paper, we propose to simulate and estimate the noise covariance structure of either a 

Decomposition of block Toeplitz matrices into a sum of Kronecker products with applications in image restoration
JG Nagy ,1996 ,
Abstract. Given a banded block Toeplitz matrix with banded Toeplitz blocks, H, an
inexpensive technique for decomposing H into a sum of Kronecker products is presented.
Applications to image restoration are discussed.