5 research outputs found
An efficient technique for lossless address data compression using adaptive SPIHT Algorithm in WSN
The computer is becoming more and more powerful day by day. Data compression is a popular approach to reducing data volumes and hence lowering disk I/O and network data transfer times. While several lossy data compression techniques have demonstrated excellent compression ratios, lossless data compression techniques are still among the most popular ones. Sensor networks represent a non-traditional source of information, as readings generated by sensors flow continuously, leading to an infinite stream of data. Sensors are non-reactive elements which are used to monitor real life phenomena, such as live weather conditions, network traffic, etc. They are usually organized into networks where their readings are transmitted using low level protocols
Adaptive Representations for Image Restoration
In the �eld of image processing, building good representation models for
natural images is crucial for various applications, such as image restora-
tion, sampling, segmentation, etc. Adaptive image representation models
are designed for describing the intrinsic structures of natural images. In
the classical Bayesian inference, this representation is often known as the
prior of the intensity distribution of the input image. Early image priors
have forms such as total variation norm, Markov Random Fields (MRF),
and wavelets. Recently, image priors obtained from machine learning tech-
niques tend to be more adaptive, which aims at capturing the natural image
models via learning from larger databases. In this thesis, we study adaptive
representations of natural images for image restoration.
The purpose of image restoration is to remove the artifacts which degrade
an image. The degradation comes in many forms such as image blurs,
noises, and artifacts from the codec. Take image denoising for an example.
There are several classic representation methods which can generate state-
of-the-art results. The �rst one is the assumption of image self-similarity.
However, this representation has the issue that sometimes the self-similarity
assumption would fail because of high noise levels or unique image contents.
The second one is the wavelet based nonlocal representation, which also has
a problem in that the �xed basis function is not adaptive enough for any
arbitrary type of input images. The third is the sparse coding using over-
complete dictionaries, which does not have the hierarchical structure that is
similar to the one in human visual system and is therefore prone to denoising
artifacts.
My research started from image denoising. Through the thorough review
and evaluation of state-of-the-art denoising methods, it was found that the representation of images is substantially important for the denoising tech-
nique. At the same time, an improvement on one of the nonlocal denoising
method was proposed, which improves the representation of images by the
integration of Gaussian blur, clustering and Rotationally Invariant Block
Matching. Enlightened by the successful application of sparse coding in
compressive sensing, we exploited the image self-similarity by using a sparse
representation based on wavelet coe�cients in a nonlocal and hierarchical
way, which generates competitive results compared to the state-of-the-art
denoising algorithms. Meanwhile, another adaptive local �lter learned by
Genetic Programming (GP) was proposed for e�cient image denoising. In
this work, we employed GP to �nd the optimal representations for local im-
age patches through training on massive datasets, which yields competitive
results compared to state-of-the-art local denoising �lters. After success-
fully dealt with the denoising part, we moved to the parameter estimation
for image degradation models. For instance, image blur identi�cation uses
deep learning, which has recently been proposed as a popular image repre-
sentation approach. This work has also been extended to blur estimation
based on the fact that the second step of the framework has been replaced
with general regression neural network. In a word, in this thesis, spatial cor-
relations, sparse coding, genetic programming, deep learning are explored
as adaptive image representation models for both image restoration and
parameter estimation.
We conclude this thesis by considering methods based on machine learning
to be the best adaptive representations for natural images. We have shown
that they can generate better results than conventional representation mod-
els for the tasks of image denoising and deblurring
Sparse image approximation with application to flexible image coding
Natural images are often modeled through piecewise-smooth regions. Region edges, which correspond to the contours of the objects, become, in this model, the main information of the signal. Contours have the property of being smooth functions along the direction of the edge, and irregularities on the perpendicular direction. Modeling edges with the minimum possible number of terms is of key importance for numerous applications, such as image coding, segmentation or denoising. Standard separable basis fail to provide sparse enough representation of contours, due to the fact that this kind of basis do not see the regularity of edges. In order to be able to detect this regularity, a new method based on (possibly redundant) sets of basis functions able to capture the geometry of images is needed. This thesis presents, in a first stage, a study about the features that basis functions should have in order to provide sparse representations of a piecewise-smooth image. This study emphasizes the need for edge-adapted basis functions, capable to accurately capture local orientation and anisotropic scaling of image structures. The need of different anisotropy degrees and orientations in the basis function set leads to the use of redundant dictionaries. However, redundant dictionaries have the inconvenience of giving no unique sparse image decompositions, and from all the possible decompositions of a signal in a redundant dictionary, just the sparsest is needed. There are several algorithms that allow to find sparse decompositions over redundant dictionaries, but most of these algorithms do not always guarantee that the optimal approximation has been recovered. To cope with this problem, a mathematical study about the properties of sparse approximations is performed. From this, a test to check whether a given sparse approximation is the sparsest is provided. The second part of this thesis presents a novel image approximation scheme, based on the use of a redundant dictionary. This scheme allows to have a good approximation of an image with a number of terms much smaller than the dimension of the signal. This novel approximation scheme is based on a dictionary formed by a combination of anisotropically refined and rotated wavelet-like mother functions and Gaussians. An efficient Full Search Matching Pursuit algorithm to perform the image decomposition in such a dictionary is designed. Finally, a geometric image coding scheme based on the image approximated over the anisotropic and rotated dictionary of basis functions is designed. The coding performances of this dictionary are studied. Coefficient quantization appears to be of crucial importance in the design of a Matching Pursuit based coding scheme. Thus, a quantization scheme for the MP coefficients has been designed, based on the theoretical energy upper bound of the MP algorithm and the empirical observations of the coefficient distribution and evolution. Thanks to this quantization, our image coder provides low to medium bit-rate image approximations, while it allows for on the fly resolution switching and several other affine image transformations to be performed directly in the transformed domain