image restoration-22

Adaptive-3D-Wiener for hyperspectral image restoration: influence on detection strategy
JM Gaucel, M Guillaume ,European Signal , 2006 ,
ABSTRACT In this paper we consider the problem of multichannel restoration. Current
multichannel least squares restoration filters utilize the assumption that the signal
autocorrelation, describing the between-channel and within-channel relationship, is 

The simulated remote sensing image acquisition and restoration based on optical sparse aperture system
ZG Wang, YX Zhang, ZX Geng, XL Sui ,Proceedings of SPIE, 2008 ,
ABSTRACT In spatial remote-sensing observation to the earth, the optical system aperture of
satellite is becoming larger and larger. But, the larger aperture lead to the more limits
constrained by manufacture costs and system loading. Optical sparse aperture imaging 

Filling holes in 3D meshes using image restoration algorithms
Proceedings of the , 2008 ,
Abstract This work describes a method for filling holes in a 3D mesh based on 2D image
restoration algorithms. Since these algorithms need an image as input, the first stage of the
method concerns a 3D to 2D transformation. By projecting the 3D surface onto a squared 

Fast Image Restoration Method Based on the Multi-Resolution Layer
CT Hsieh, YL Chen ,Tamkang Journal of Science and , 2009 ,
Abstract When transmitted through a poor quality network or stored on an unstable storage
media, block-based code images will experience the block loss. To restore damaged
images suffering from block loss, Best Neighborhood Matching and Jump and Look-

On Current Strategies for Hardware Acceleration of Digital Image Restoration Filters
S Catudal, R Grou, MM Mbaye ,WSEAS Transaction on , 2004 ,
Abstract:-Two advanced design methodologies for hardware acceleration of a standard
digital image restoration algorithm are explored and compared. The first one is the custom-
designed hardware approach, leading to an application-specific integrated circuit (ASIC) 

Blur and Image Restoration of Nonlinearly Degraded Images Using Neural Networks Based on Modified Nonlinear ARMA Model
TA Cheema, IM Qureshi, A Jalil ,Arabian Journal for , 2007 ,
ABSTRACT In this paper, an image restoration algorithm is proposed to identify nonlinear
and noncausal blur funclon using artificial neural networks. Image and degradation
processes include both linear and nonlinear phenomena. The proposed neural network 

Image restoration using statistical wavelet models
J Liu ,Proc. of SPIE, 2001 ,
ABSTRACT In this paper, we propose an image restoration algorithm based on state-of-the-
art wavelet domain statistical models. We present an efficient method to estimate the model
parameters from the observations, and solve the restoration problem in orthonormal and 

Salt and Pepper Noise Detection and removal by Tolerance based selective Arithmetic Mean Filtering Technique for image restoration
S Kaisar ,IJCSNS,(June, 2008), 2008 ,
Summary Removal of noises from the images is a critical issue in the field of digital image
processing. This paper proposes a Tolerance based Arithmetic Mean Filtering Technique to
remove salt and pepper noise from corrupted images. Arithmetic Mean filtering technique 

A Modified Mean Shift Algorithm For Efficient Document Image Restoration
F Drira, F Lebourgois ,Signal Processing for Image , 2008 ,Springer
Previous formulations of the global Mean Shift clustering algorithm incorporate a global
mode finding which requires a lot of computations making it extremely time-consuming. This
paper focuses on reducing the computational cost in order to process large document 

Iterative Lavrentiev regularization for symmetric kernel-driven operator equations: with application to digital image restoration problems
Y Wang, X Gu, T Yu ,Science in China Series F: Information , 2005 ,Springer
Abstract The symmetric kernel-driven operator equations play an important role in
mathematical physics, engineering, atmospheric image processing and remote sensing
sciences. Such problems are usually ill-posed in the sense that even if a unique solution 

Positively constrained total variation penalized image restoration
H LIANG ,Advances in Adaptive Data , 2011 ,
The total variation (TV) minimization models are widely used in image processing, 16 mainly
due to their remarkable ability in preserving edges. There are many methods 17 for solving
the TV model. These methods, however, seldom consider the positivity con-18 straint one 

Extractive support vector algorithm on support vector machines for image restoration
CC Yao, PT Yu ,Fundamenta Informaticae, 2009 ,IOS Press
The major problem of SVMs is the dependence of the nonlinear separating surface on the
entire dataset which creates unwieldy storage problems. This paper proposes a novel
design algorithm, called the extractive support vector algorithm, which provides improved 

Image restoration using adaptive Gaussian scale mixtures in overcomplete pyramids
J Portilla , of SPIE, the International Society for , 2007 ,
ABSTRACT We describe here two ways to improve on recent results in image restoration
using Bayes least squares estimation with local Gaussian scale mixtures (BLS-GSM) in
overcomplete oriented pyramids. First one consists of allowing for a spatial adaptation of

Image restoration of calibration and validation for KOMPSAT-2
DH Leea, DC Seo, JH Song, JH Chung , Archives of the , 2008 ,
ABSTRACT: The image restoration of Calibration and Validation (Cal/Val) for the KOMPSAT-
2 (KOrea Multi-Purpose SATellite-2) has six parameters; Linearity, Video processor
gain/offset, Non-uniformity correction, De-noising, Butting zone, SNR and additionally MTF

The Bussgang algorithm for multichannel blind image restoration
G Panci, P Campisi, S Colonnese ,XI Eur. Signal Processing , 2002 ,
ABSTRACT This work derives the multichannel Bussgang restoration algorithm for blind
image restoration problems. In its basic outline, the derived Bussgang restoration algorithm
is based on iteratively filtering the measurements by means of a bank of FIR filters, and 

Implementation and Analysis of Image Restoration Techniques
C Khare , Trends and Technology-May to June, 2011 ,
Abstract—IMAGE restoration is an important issue in high-level image processing. Images
are often degraded during the data acquisition process. The degradation may involve
blurring, information loss due to sampling, quantization effects, and various sources of 

Image Restoration Using Piecewise Iterative Curve Fitting and Texture Synthesis
K Sun, Y Yang, L Ye ,Advanced Intelligent Computing Theories , 2008 ,Springer
In this paper, we present a novel scheme for parsing images into medium level vision
representation: contour curve and region texture. This scheme is integrated with piecewise
iterative curve fitting and texture synthesis, in which an original image is analyzed at the 

A MRF based Approach to Color Image Restoration
EK Stringa ,Proc. of VIII European ,
ABSTRACT In this paper, a Markov Random Field (MRF)-based method is presented. MRF
methods are based on a probabilistic representation of a image processing problem; the
problem is represented as the maximization of a probability measure computed starting 

Real-Time Adaptive Filtering for Nonstationary Image Restoration Using Gaussian Input
A ABILOVV, O TUZUNALPVV , -MED99 Proceedings of , 1999 ,
A new real-time adaptive filter algorithm is presented for the restoration of the images which
are degraded by the Atmospheric turbulence or imaging systems. Filter model parameters of
the proposed algorithm adaptively converge degradation model parameter in a given time 

Assessment of image restoration techniques to enhance the applicability of MODIS images on Amazon floodplain landscape studies
Instituto Nacional de , 2007 ,
Abstract. The Amazon floodplain represents a significant portion of the world’s wetlands, and
participates actively in the carbon cycling in the region. Due to its large extent, remote
sensing is the most appropriate tool for studying the Amazonian landscape; image

Estimating radar cross section using Bayesian image restoration
Proceedings of the London Communications , 2003 ,
Abstract: This paper describes a numerical Bayesian technique for computing the two-
dimensional radar cross section (RCS) of a vehicle given a radar image of the vehicle. A
Markov chain Monte Carlo technique known as the Metropolis-Hastings algorithm is used