Nonlinear Inverse Scale Space Methods for Image Restoration

2005 ◽  
pp. 25-36 ◽  
Author(s):  
Martin Burger ◽  
Stanley Osher ◽  
Jinjun Xu ◽  
Guy Gilboa
Keyword(s):  
Image Restoration ◽  
Scale Space ◽  
Inverse Scale Space

Generalized Probabilistic Scale Space for Image Restoration

2010 ◽  
Vol 19 (10) ◽  
pp. 2774-2780 ◽  
Author(s):  
Alexander Wong ◽  
Akshaya K Mishra
Keyword(s):  
Image Restoration ◽  
Scale Space

AN ADAPTIVE DIFFUSION SCHEME FOR IMAGE RESTORATION AND SELECTIVE SMOOTHING

2012 ◽  
Vol 12 (01) ◽  
pp. 1250003 ◽  
Author(s):  
V. B. SURYA PRASATH ◽  
ARINDAMA SINGH
Keyword(s):  
Image Restoration ◽  
Impulse Noise ◽  
Scale Space ◽  
Edge Information ◽  
Spatial Function ◽  
Diffusion Scheme ◽  
Gradient Based ◽  
Local Edge ◽  
Well Posed ◽  
Selective Smoothing

Anisotropic partial differential equation (PDE)-based image restoration schemes employ a local edge indicator function typically based on gradients. In this paper, an alternative pixel-wise adaptive diffusion scheme is proposed. It uses a spatial function giving better edge information to the diffusion process. It avoids the over-locality problem of gradient-based schemes and preserves discontinuities coherently. The scheme satisfies scale space axioms for a multiscale diffusion scheme; and it uses a well-posed regularized total variation (TV) scheme along with Perona-Malik type functions. Median-based weight function is used to handle the impulse noise case. Numerical results show promise of such an adaptive approach on real noisy images.

Fast sparse reconstruction: Greedy inverse scale space flows

2015 ◽  
Vol 85 (297) ◽  
pp. 179-208 ◽  
Author(s):  
Michael Moeller ◽  
Xiaoqun Zhang
Keyword(s):  
Scale Space ◽  
Sparse Reconstruction ◽  
Inverse Scale Space

ITERATIVE REGULARIZATION AND NONLINEAR INVERSE SCALE SPACE BASED ON TRANSLATION INVARIANT WAVELET SHRINKAGE

2008 ◽  
Vol 06 (01) ◽  
pp. 83-95 ◽  
Author(s):  
MIN LI ◽  
BINBIN HAO ◽  
XIANGCHU FENG
Keyword(s):  
Wavelet Decomposition ◽  
Scale Space ◽  
Regularization Methods ◽  
Wavelet Shrinkage ◽  
Iterative Regularization ◽  
Translation Invariant ◽  
New Class ◽  
New Methods ◽  
Scale Spaces ◽  
Inverse Scale Space

In this paper, we present a new class of iterative regularization methods in the setting of Besov spaces, which can be seen as generalizations of J. Xu's method. By incorporating translation invariant wavelet transform, minimizers of the new methods can be understood as the alternative to translation invariant wavelet shrinkage with weight that is dependent on the wavelet decomposition scale and the Besov smooth order. And we generalize the iterative regularization methods to a new class of nonlinear inverse scale spaces with scale and Besov smooth order dependent weight. The numerical results show an excellent denoising effect and improvement over J. Xu's method.

scholarly journals An adaptive inverse scale space method for compressed sensing

2012 ◽  
Vol 82 (281) ◽  
pp. 269-299 ◽  
Author(s):  
Martin Burger ◽  
Michael Möller ◽  
Martin Benning ◽  
Stanley Osher
Keyword(s):  
Compressed Sensing ◽  
Scale Space ◽  
Space Method ◽  
Inverse Scale Space
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