image restoration-33




A partitioned modified Hopfield neural network algorithm for real-time image restoration
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SW Perry ,REAL TIME IMAGING, 1996 ,rml.ryerson.ca
In recent years attention has been turned to the use of neural network-derived algorithms to
restore images using a model-based approach. Considering an M by M input image, in most
cases the image degradation model is a spatially and temporally invariant linear distortion 

Image restoration for quantifying TFT-LCD defect levels
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KN Choi, NK Park ,IEICE transactions on information and , 2008 ,agent.snu.ac.kr
SUMMARY Though machine vision systems for automatically detecting visual defects, called
mura, have been developed for thin flat transistor liquid crystal display (TFT-LCD) panels,
they have not yet reached a level of reliability which can replace human inspectors. To 

Statistical mechanics of image restoration by the plane rotator model
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Y Saika ,Journal of the Physics Society Japan, 2002 ,stat.phys.titech.ac.jp
On the basis of statistical mechanics formulation for problems of image restoration and
errorcorrecting codes, we propose a new technique of image restoration for a binary image
using the plane rotator model. In our formulation, the restored image is obtained from the 

A blind deconvolution algorithm for simultaneous image restoration and system characterisation
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M Razaz ,Signal Processing VIII: Theories , 1996 ,eurasip.org
ABSTRACT The restoration of a blurred image in a practical imaging system is critically
dependent on the system point spread function. Measurement of the point spread function is
often a difficult and time consuming process, and the measurement environment itself is 

Quantitative image restoration
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I Gladkova, M Grossberg ,Proceedings of the SPIE, , 2010 ,csdirs.ccny.cuny.edu
ABSTRACT Even with the most extensive precautions and careful planning, space based
imagers will inevitably experience problems resulting in partial data corruption and possible
loss. Such a loss occurs, for example, when individual image detectors are damaged. For 

Blur identification using neural network for image restoration
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Intelligence, Theory and , 2006 ,Springer
Summary. A prior knowledge about the distorting operator and its parameters is of crucial
importance in blurred image restoration. In this paper the continuous-valued multilayer
neural network based on multivalued neurons (MLMVN) is exploited for identification of a 

Landsat tm satellite image restoration using kalman filters
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D Arbel, E Cohen, M Citroen , and remote sensing, 2004 ,beta.asprs.org
Abstract The quality of satellite images propagating through the atmosphere is affected by
phenomena such as scattering and absorption of light, and turbulence, which degrade the
image by blurring it and reducing its contrast. The atmospheric Wiener filter, which 

Spectral amplitude estimation-based X-ray image restoration: An extension of a speech enhancement approach
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T Aach ,Proc. EUSIPCO-98, Rodos, Greece, 1998 ,eurasip.org
ABSTRACT This paper describes a class of spectral amplitude estimation-based algorithms
for the restoration of low dose X-ray images. Since estimation of spectral amplitude from
noisy observations is a widely reported approach to restore noisy speech signals, we 

Multichannel image identification and restoration using the expectation-maximization algorithm
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BC Tom, AK Katsaggelos ,Optical Engineering, 1996 ,ivpl.ece.northwestern.edu
Abstract. Previous work has demonstrated the effectiveness of the expectation-maximization
algorithm to restore noisy and blurred singlechannel images and simultaneously identify its
blur. In addition, a general framework for processing multichannel images using single-

Image restoration viawiener filtering in the frequency domain
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H Furuya, S Eda ,WSEAS Transactions on Signal , 2009 ,wseas.us
Abstract:-In this paper, first, the performance of the Wiener filter in the frequency domain for
image restoration is compared with that in the space domain on images degraded by white
noise. After finding that the Wiener filter in the frequency domain is more effective than that 

Hyperparameter estimation using hyperpriors for hierarchical Bayesian image restorationfrom partially-known blurs
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NP Galatsanos, VZ Mesarovic, R Molina ,Proc. , 1998 ,ivpl.ece.northwestern.edu
ABSTRACT In this paper we examine the problem of estimating the hyperparameters in
image restoration when the pointspread function (PSF) of the degradation system is partially
known. For this problem the PSF is assumed to be the sum of a known deterministic and 

Multichannel blind image restoration
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HT Pai   1999 ,ntpu.edu.tw
Abstract Images may be degraded for many reasons. For example, out-of-focus optics
produce blurred images, and variations in electronic imaging components introduce noise.
Reducing blur and noise in images is known as image restoration. In this paper, we first 

Computational methods for image restoration, image segmentation, and texture modeling
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G Chung, TM Le, LH Lieu, NM Tanushev ,Computational , 2006 ,math.ucla.edu
ABSTRACT This work is devoted to new computational models for image segmentation,
image restoration and image decomposition. In particular, we partition an image into
piecewise-constant regions using energy minimization and curve evolution approaches. 

A nonlinear primal-dual method for total variation-based image restoration
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T Chan, G Golub ,ICAOS’96, 1996 ,Springer
We present a new method for solving total variation (TV) minimization problems in image
restoration. The main idea is to remove some of the singularity caused by the non-
differentiability of the quantity| u| in the definition of the TV-norm before we apply a 

An iterative regularization method for total variation-based image restoration
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S Osher, M Burger, D Goldfarb  Multiscale Modeling and , 2005 ,rush.edu
Abstract. We introduce a new iterative regularization procedure for inverse problems based
on the use of Bregman distances, with particular focus on problems arising in image
processing. We are motivated by the problem of restoring noisy and blurry images via 

Image Decomposition and Restoration Using Total Variation Minimization and the H^-^ 1 Norm
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S Osher, A Solé ,Multiscale Modeling and Simulation, 2003 ,math.ucla.edu
Abstract. In this paper, we propose a new model for image restoration and image
decomposition into cartoon and texture, based on the total variation minimization of Rudin,
Osher, and Fatemi [Phys. D, 60 (1992), pp. 259–268], and on oscillatory functions, which 

The dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement
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NG Kingsbury ,Proc. EUSIPCO, 1998 ,eurasip.org
ABSTRACT A new implementation of the Discrete Wavelet Transform is presented for
applications such as image restoration and enhancement. It employs a dual tree of wavelet
lters to obtain the real and imaginary parts of the complex wavelet coe cients. This 

Maximum entropy image restoration in astronomy
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N Ramesh ,Annual review of astronomy and , 1986 ,repository.ias.ac.in
Imaging the two-dimensional intensity distribution of the sky has always been an important
part of astronomy. This is particularly true at present, a time when aperture synthesis
mapping is ?rmly established in radio astronomy, charge-coupled devices are 

Iterative image restoration algorithms
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A KATSAGGELOS ,Optical engineering, 1989 ,58.192.112.18
In this chapter we consider a class of iterative restoration algorithms. If y is the observed noisy
and blurred signal, D the operator describing the degradation system, x the input to the
system, and n the noise added to the output signal, the input-output relation is described 

Image restoration with discrete constrained total variation part I: Fast and exact optimization
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,Journal of Mathematical Imaging and Vision, 2006 ,Springer
Abstract This paper deals with the total variation minimization problem in image restoration
for convex data fidelity functionals. We propose a new and fast algorithm which computes an
exact solution in the discrete framework. Our method relies on the decomposition of an 

Second-order cone programming methods for total variation-based image restoration
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D Goldfarb ,SIAM Journal on Scientific Computing, 2005 ,optimization-online.org
Abstract. In this paper we present optimization algorithms for image restoration based on the
total variation (TV) minimization framework of L. Rudin, S. Osher and E. Fatemi (ROF). Our
approach formulates TV minimization as a second-order cone program which is then 

Solar image restoration by use of multi-frame blind de-convolution with multiple objects and phase diversity
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M Van Noort, LRV Der Voort ,Solar Physics, 2005 ,Springer
Abstract An extension of Joint Phase Diverse Speckle image restoration is presented.
Multiple realizations of multiple objects having known wavefront relations with each other
can now be restored jointly. As the alignment of the imaging setup does not change, near-

A l 1-unified variational framework for image restoration
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L Blanc-Féraud, G Aubert ,Computer Vision-ECCV , 2004 ,Springer
Among image restoration literature, there are mainly two kinds of approach. One is based on
a process over image wavelet coefficients, as wavelet shrinkage for denoising. The other
one is based on a process over image gradient. In order to get an edge-preserving 

Recent developments in total variation image restoration
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T Chan, S Esedoglu, F Park ,Mathematical Models of , 2005 ,math.ucla.edu
ABSTRACT Since their introduction in a classic paper by Rudin, Osher and Fatemi [26], total
variation minimizing models have become one of the most popular and successful
methodology for image restoration. More recently, there has been a resurgence of interest 


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