image restoration-41

Very high quality image restoration by combining wavelets and curvelets
JL Starck, DL Donoho ,Proc. SPIE, 2001 ,
ABSTRACT We outline digital implementations of two newly developed multiscale
representation systems, namely, the ridgelet and curvelet transforms. We apply these digital
transforms to the problem of restoring an image from noisy data and compare our results 

An efficient primal-dual hybrid gradient algorithm for total variation image restoration
M Zhu ,UCLA CAM Report, 2008 ,
Abstract We propose a simple yet efficient algorithm for total variation (TV) minimizations
with applications in the image processing realm. This descent-type algorithm alternates
between the primal and dual formulations and exploit the information from both the primal 

Dual methods for total variation-based image restoration
JL Carter ,2001 ,
3.1 Primal relaxation does remove some of the high-frequency noise, but it converges to the
wrong solution. 19 3.2 When we initialize primal relaxation at the Chan-Golub-Mulet
solution with large ß, it does sharpen the edges, but it still gives the wrong answer

Iterative image restoration combining total variation minimization and a second-order functional
M Lysaker ,International Journal of Computer Vision, 2006 ,Springer
Abstract A noise removal technique using partial differential equations (PDEs) is proposed
here. It combines the Total Variational (TV) filter with a fourth-order PDE filter. The combined
technique is able to preserve edges and at the same time avoid the staircase effect in 

Mathematical analysis of a model which combines total variation and wavelet for image restoration
F Malgouyres ,Journal of information processes, 2002 ,
Abstract—We give a mathematical analysis of a model previously introduced for image
restoration. This model combines wavelet approaches and total variation approaches in a
natural way. We prove the existence of a solution to the model. Then we show a way to 

Image restoration with discrete constrained total variation part ii: Levelable functions, convex priors and non-convex cases
Journal of Mathematical Imaging and Vision, 2006 ,Springer
Abstract In Part II of this paper we extend the results obtained in Part I for total variation
minimization in image restoration towards the following directions: first we investigate the
decomposability property of energies on levels, which leads us to introduce the concept of 

Image restoration and decomposition via bounded total variation and negative Hilbert-Sobolev spaces
LH Lieu ,Applied Mathematics & Optimization, 2008 ,Springer
Abstract We propose a new class of models for image restoration and decomposition by
functional minimization. Following ideas of Y. Meyer in a total variation minimization
framework of L. Rudin, S. Osher, and E. Fatemi, our model decomposes a given (

Electron detection characteristics of a slow-scan CCD camera, imaging plates and film, and electron image restoration
JM Zuo ,Microscopy research and technique, 2000 ,
ABSTRACT Electron detection characteristics are summarized for the slow scan CCD (SSC)
camera, imaging plates, and film. The advantage of each detector is demonstrated with the
selected examples of electron diffraction and imaging. The Richardson-Lucy algorithm for 

A fast algorithm for edge-preserving variational multichannel image restoration
J Yang, W Yin, Y Zhang ,SIAM Journal on Imaging , 2009 ,
Abstract. Variational models with i1-norm based regularization, in particular total variation
(TV) and its variants, have long been known to offer superior image restoration quality, but
processing speed remained a bottleneck, preventing their widespread use in the practice 

Robust priors for smoothing and image restoration
HR Künsch ,Annals of the Institute of Statistical Mathematics, 1994 ,Springer
The Bayesian method for restoring an image corrupted by added Gaussian noise uses a
Gibbs prior for the unknown clean image. The potential of this Gibbs prior penalizes
differences between adjacent grey levels. In this paper we discuss the choice of the form 

Regularization in image restoration and reconstruction
WC Karl ,Handbook of Image and Video Processing, 2000 ,
This chapter focuses on the need for and use of regularization methods in the solution of
image restoration and reconstruction problems. The methods discussed here are applicable
to a variety of such problems. These applications to specific problems, including 

Duality-based algorithms for total-variation-regularized image restoration
M Zhu  Computational Optimization and Applications, 2010 ,Springer
Abstract Image restoration models based on total variation (TV) have become popular since
their introduction by Rudin, Osher, and Fatemi (ROF) in 1992. The dual formulation of this
model has a quadratic objective with separable constraints, making projections onto the 

Nonlinear inverse scale space methods for image restoration
M Burger, S Oshe  Geometric, and Level Set Methods in , 2005 ,Springer
In this paper we generalize the iterated refinement method, introduced by the authors in [8],
to a time-continuous inverse scale-space formulation. The iterated refinement procedure
yields a sequence of convex variational problems, evolving toward the noisy image. The 

Some first-order algorithms for total variation based image restoration
JF Aujol ,Journal of Mathematical Imaging and Vision, 2009 ,Springer
Abstract This paper deals with first-order numerical schemes for image restoration. These
schemes rely on a duality-based algorithm proposed in 1979 by Bermùdez and Moreno.
This is an old and forgotten algorithm that is revealed wider than recent schemes (such as 

Constrained and unconstrained PDEs for vector image restoration
ABSTRACT The restoration of noisy and blurred scalar images has been widely studied,
and many algorithms based on variational or stochastic formulations have tried to solve this
ill-posed problem

TV based image restoration with local constraints
V Caselles, B Rougé ,Journal of scientific , 2003 ,Springer
The problem of recovering an image that has been blurred and corrupted with additive noise
is ill-posed. Among the methods that have been proposed to solve this problem, one of the
most successful ones is that of constrained Total Variation (TV) image restoration, 

Variational pairing of image segmentation and blind restoration
L Bar, N Sochen ,Computer Vision-ECCV 2004, 2004 ,Springer
Segmentation and blind restoration are both classical problems, that are known to be difficult
and have attracted major research efforts. This paper shows that the two problems are tightly
coupled and can be successfully solved together. Mutual support of the segmentation and 

Image restoration and classification by topological asymptotic expansion
D Auroux, M Masmoudi , in mechanics: theory , 2006 ,
Abstract. We present in this paper a new way for modeling and solving image restoration
and classification problems, the topological gradient method. This method is considered in
the frame of variational approaches and the minimization of potential energy with respect