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BCS-SPL algorithm for Compressive Sensing
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BCS-SPL combines block-based compressed- sensing sampling (BCS) of an image with a smoothed projected-Landweber (SPL) iterative reconstruction. Sampling is driven by random matrices applied on a block-by-block basis, while the reconstructio
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Image modelling markov random field
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Image modeling plays an important role in
modern image processing. It is commonly used in image analysis for texture
segmentation, classification and. It is also of common use in multimedia
applications for compression purposes. Due to the diversity of image types, it
is impossible to have one universal model to cover all the possible types, even
when limiting ourselves to simple texture cases. Various models have been
proposed and each has its advantages and drawbacks. For textures, co-occurrence
matrices are a standard choice but they are in general difficult to compute and
are difficult to use for modeling. We will concentrate here on Markov Random
Field (MRF) which have been widely and very successfully used in the last 15
years.
Markov random fields belong to the
statistical models of images. Each pixel of an image can be viewed as a random
variable. An image c
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Block_Base_Compressed_sensing
5.0
Block-based random image sampling is coupled with a projectiondriven
compressed-sensing recovery that encourages sparsity in
the domain of directional transforms simultaneously with a smooth
reconstructed image. Both contourlets as well as complex-valued
dual-tree wavelets are considered for their highly directional representation,
while bivariate shrinkage is adapted to their multiscale
decomposition structure to provide the requisite sparsity constraint.
Smoothing is achieved via a Wiener filter incorporated
into iterative projected Landweber compressed-sensing recovery,
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