We propose a numerical strategy to define a multiscale analysis for color and multicomponent images based on the representation of data on a graph. Our approach consists of computing the graph of an image using the psychovisual information and analyzing it by using the spectral graph wavelet transform. We suggest introducing color dimension into the computation of the weights of the graph and using the geodesic distance as a mean of distance measurement. We thus have defined a wavelet transform based on a graph with perceptual information by using the CIELab color distance. This new representation is illustrated with denoising and inpainting applications. Overall, by introducing psychovisual information in the graph computation for the graph wavelet transform, we obtain very promising results. Thus, results in image restoration highlight the interest of the appropriate use of color information.
We present a steganographic scheme based on the contourlet transform which uses the contrast sensitivity function
(CSF) to control the force of insertion of the hidden information in a perceptually uniform color space.
The CIELAB color space is used as it is well suited for steganographic applications because any change in the
CIELAB color space has a corresponding effect on the human visual system as is very important for steganographic
schemes to be undetectable by the human visual system (HVS). The perceptual decomposition of the
contourlet transform gives it a natural advantage over other decompositions as it can be molded with respect
to the human perception of different frequencies in an image. The evaluation of the imperceptibility of the
steganographic scheme with respect to the color perception of the HVS is done using standard methods such as
the structural similarity (SSIM) and CIEDE2000. The robustness of the inserted watermark is tested against
An online buyer of multimedia content does not want to reveal his identity or his choice of multimedia content
whereas the seller or owner of the content does not want the buyer to further distribute the content illegally.
To address these issues we present a new private anonymous fingerprinting protocol. It is based on superposed
sending for communication security, group signature for anonymity and traceability and single database private
information retrieval (PIR) to allow the user to get an element of the database without giving any information
about the acquired element. In the presence of a semi-honest model, the protocol is implemented using a blind,
wavelet based color image watermarking scheme. The main advantage of the proposed protocol is that both the
user identity and the acquired database element are unknown to any third party and in the case of piracy, the
pirate can be identified using the group signature scheme. The robustness of the watermarking scheme against
Additive White Gaussian Noise is also shown.
Nowadays one of the most important issue linked to image transform is to take
into account the singularities of a signal which is organized on more than one
dimension. The best example is the wavelet transform extension to two
dimensional signal analysis. The drawback when one pass from a one dimensional
signal process to a two dimensional signal process by simply using separability of
wavelet transform is the over representation of irregularities in the wavelet
transform domain. In order to decrease this drawback, second generation wavelet
transform tries to take geometrical aspects of the image into account in the
analysis of the image (one can find examples with bandelets, curvelets,
ridgelets and others).
2 layers bandelets or first generation bandelets is among the first wavelet
transform which uses the flow to enhance the efficiency of the process. The
present proposition is mainly theoretical : we will propose now a pratical
interpretation of this work in order to make a new implementation of the
The recurrent presence of clouds and clouds shadows in aerial or remotely sensed images is an awkward
problem that severely limits the regular exploitations capability of these images. Removing cloud-contaminated
portions of the image and then filling in the missing data represent an important photo editing cumbersome task.
The intent of this work is to propose a technique for the reconstruction of areas obscured by clouds in a remotely
sensed image. To this end, a new efficient reconstruction technique for missing data synthesis is presented.
This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two
steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the
Bandelet transform with multiscale grouping. This step allows a better representation of the multiscale geometry
of the image's structures. Having well represented this geometry, the information inside the cloud-contaminated
zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by
minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of
the size and topology of the reconstruction or inpainting domain. Thus, the flow lines are well tied inside the
cloud-contaminated zone. The proposed technique is illustrated with some examples on processing multispectral
aerial images. The obtained results are compared with those obtained by other clouds removal techniques.
Quaternionic Wavelet Transform (QWT) already exist but it dealt with greyscale images. In this paper we propose a quaternionic wavelet transform aimed to colour image processing. To encode colour information in our new transformation, a pixel in the spatial domain is represented by a quaternion as described by Sangwine. First, we propose to use the discrete quaternionic Fourier transform to study the spectral information of the colour image. It is well known that the frequency space of a real signal is a complex hermitian signal, we then studied the digital spectral domain of the quaternionic Fourier transform in order to analyze symmetry properties. This study gives us one characterization of the colour Fourier domain. Second we use the quaternion formalism to define a wavelet transform for colour images. We propose to generalize the filter bank construction to quaternionic formalism. Especially, we describe conditions on quaternionic filters to obtain a perfect reconstruction. We build a first colour quaternionic filter bank: the colour Shannon Wavelet. This family of functions are based on a windowing process in the quaternionic Fourier space.
When characterizing textures in the scope of recognition or segmentation, one can choose from a great number of
existing features. Among them, features based on the wavelet decomposition provide good results and are already
used in many applications. One key point for the success of these methods is the choice of the signature used
to describe the sub-bands. The energy signature is the most popular, but others exist, with better efficiency. In
this paper, we review some of them and bring improvements in their computation. We also show that combining
spatial and statistical signatures increase their performance in texture classification problematics.
In this paper, we review an implementation of the Ridgelet transform: The Discrete Analytical Ridgelet Transform (DART). This transform uses the Fourier strategy for the computation of the associated 2-D and 3-D discrete Radon transforms. The innovative step is the definition of a discrete 3-D transform and the construction of discrete analytical lines in the Fourier domain. These discrete analytical lines have a parameter called arithmetical thickness, allowing us to define a DART adapted to a specific application. Indeed, the DART representation is not orthogonal, it is associated with a flexible redundancy factor. The DART has a very simple forward/inverse algorithm that provides an exact reconstruction without any iterative method. We had proved in different publications that the 2D and 3D DART are performant for the level of greys images restorations. Therefore we have interesting to 2D/3D color image restorations. We have compared the restoration results in function of different color space definition and importance of the white Gaussian noise. We criticize our results with two different measures : the Signal Noise Ratio calculation and perceptual measures to evaluate the perceptual colour difference between original and denoised images. These experimental results show that the simple thresholding of the DART coefficients is competitive than classical denoising techniques.
Our article presents a new way to characterize texture : Wavelet Geometrical Features, that extracts structural measurements from wavelet sub-bands, when most of the wavelet-based methods found in the litterature use only statistical ones. We first describe the method used to compute our features, and thereafter compare them
to thirteen other standard texture features in a classification experiment on the whole Brodatz texture database. We showed that our method produces the best results, especially over the wavelet energy signature and the method it originated from, the Statistical Geometrical Features of Chen.
This paper addresses the problem of quality assessment dedicated to two important applications : image compression and watermarking. This topic is nowadays of a great interest because of the limitations of the mathematical criteria used formerly for quality assessment. The main aspect of this paper is the use of psychophysical experiences
in order to take into account the capacities of the Human Visual System. Two campaigns have been taken for assess quality for both compression and watermarking. The main conclusion of this work is that the metrics used to assess quality such as the PSNR are very far from the human judgment and consequently from the real assessment.
In this paper, we propose a new decomposition scheme for spatially adaptive wavelet packets. Contrary to the double tree algorithm, our method is non-uniform and shift- invariant in the time and frequency domains, and is minimal for an information cost function. We prose some-restrictions to our algorithm to reduce the complexity and permitting us to provide some time-frequency partitions of the signal in agreement with its structure. This new 'totally' non-uniform transform, more adapted than Malvar, Packets or dyadic double-tree decomposition, allows the study of all possible time-frequency partitions with the only restriction that the blocks are rectangular. It permits one to obtain a satisfying Time-Frequency representation, and is applied for the study of EEG signals.
In this paper, we propose an original decomposition scheme based on Meyer's wavelets. In opposition to a classical technique of wavelet packet analysis, the decomposition is an adaptative segmentation of the frequential axis which does not use a filters bank. This permits a higher flexibility in the band frequency definition. The decomposition computes all possible partitions from a sequential space: it does not only compute those that come from a dyadic decomposition. Our technique is applied on the electroencephalogram signal; here the purpose is to extract a best basis of frequential decomposition. This study is part of a multimodal functional cerebral imagery project.