Old movies suffer from several degradations mainly due to the archiving conditions. Since most of the old
films represent an important amount of valuable data in scientific, cultural, social and economical purposes, it
is mandatory to preserve them by resorting to fully digital and automatic restoration techniques. Among the
existing degradations, blotches have been found to be very visually unpleasant artifacts and hence, many efforts
have been devoted to design blotch correction procedures. Generally, two-step restoration procedures have been
investigated: a blotch detection is performed prior to the correction of the degradation. The contribution of our
approach is twofold. Firstly, the blotch detection is carried out on a multiscale representation of the degraded
frames. Secondly, statistical tests are employed to locate the underlying artifacts. In this paper, we aim at
achieving two objectives. In one hand, we improve the detection performances by exploiting into the statistical
test, the interscale dependencies existing between the coefficients of the considered multiscale representation of
the underlying frames. In the other hand, an efficient spatio-temporal inpainting-based technique of filling-in
missing areas is used in order to estimate the information masked by the blotches. Experimental results indicate
the efficiency of our approach compared to conventional blotch correction methods.
In this work, we address the problem of multichannel image retrieval in the compressed domain. A wavelet
transform is applied to each component of the multispectral image. The salient features are computed from
the resulting wavelet subbands. To this purpose, two approaches are envisaged. In the first one, the wavelet
coeffcients of each component are separately considered whereas in the second one, they are jointly processed.
More precisely, the contribution of this work lies on the fact that the features are extracted from the multivariate
distribution of the wavelet coeffcients modelized thanks to copulas. Experimental results indicate that the
second approach gives the best performances in terms of precision and recall.
Both reduction of the bone mass and a degradation of the microarchitecture of the bone tissue are indicators of
the osteoporosis disease. This is why radiographies of the calcaneus are very often used in order to analyze and
describe both the texture and the structure of the bone. Therefore, a great effort is devoted to texture analysis
by sophisticated image processing tools. In this paper, we propose a method for extracting information from a
multiresolution representation of the radiological images that facilitates the graphic detection of the osteoporosis.
The main contribution of this work relies on the statistical processing of the wavelet-based extracted features that
are employed to graphically discriminate between stwo kinds of Osteoporotic Patients (OP1: vertebral fracture,
OP2: other fractures) and Control Patients (CP). Graphical discrimination is obtained by an estimation of
patients classes' densities by a multivariate kernel density estimation method, the axes result from a linear
discriminant analysis between OP1/CP and OP2/CP. Classification and statistical tests carried out on a set
of radiographies with their own ground truth validate the ability of discrimination of the proposed features
extracted from M-band wavelet transform
The objective of this paper is to design a new estimator for multicomponent image denoising in the wavelet transform domain. To this end, we extend the block-based thresholding method initially proposed by Cai and Silverman, which takes advantage of the spatial dependence between the wavelet coefficients. In the case of multispectral images, we develop a more general framework for block-based shrinkage, the blocks being built from various combinations
of the wavelet coefficients of the different image channels at adjacent spatial positions, for a given orientation and resolution level. In the presence of possibly spectrally correlated Gaussian noise, the parameters of the resulting estimator are optimized from the data by exploiting Stein's principle. Simulations show the higher performance of our estimator for denoising multispectral satellite images.
In this paper, we are interested in designing lifting schemes adapted to the statistics of the wavelet coefficients of multiband images for compression applications. More precisely, nonseparable vector lifting schemes are used in order to capture simultaneously the spatial and the spectral redundancies. The underlying operators are then computed in order to minimize the entropy of the resulting multiresolution representation. To this respect, we have developed a new iterative block-based classification algorithm. Simulation tests carried out on remotely sensed multispectral images indicate that a substantial gain in terms of bit-rate is achieved by the proposed adaptive coding method w.r.t the non-adaptive one.
In this paper, we introduce vector-lifting schemes which allow to generate very compact multiresolution representations, suitable for lossless and progressive coding of multispectral images. These new decomposition schemes exploit simultaneously the spatial and the spectral redundancies contained in multispectral images. When the spectral bands have different dynamic ranges, we improve dramatically the performances of the proposed schemes by a reversible histogram modification based on sorting permutations. Simulation tests carried out on real images allow to evaluate the performances of this new compression method. They indicate that the achieved compression ratios are higher than those obtained with currently used lossless coders.
Huge amounts of data are generated thanks to the continuous improvement of remote sensing systems. Archiving this tremendous volume of data is a real challenge which requires lossless compression techniques. Furthermore, progressive coding constitutes a desirable feature for telebrowsing. To this purpose, a compact and pyramidal representation of the input image has to be generated. Separable multiresolution decompositions have already been proposed for multicomponent images allowing each band to be decomposed separately. It seems however more appropriate to exploit also the spectral correlations. For hyperspectral images, the solution is to apply a 3D decomposition according to the spatial and to the spectral dimensions. This approach is not appropriate for multispectral images because of the reduced number of spectral bands. In recent works, we have proposed a nonlinear subband decomposition scheme with perfect reconstruction which exploits efficiently both the spatial and the spectral redundancies contained in multispectral images. In this paper, the problem of coding the coefficients of the resulting subband decomposition is addressed. More precisely, we propose an extension to the vector case of Shapiro's embedded zerotrees of wavelet coefficients (V-EZW) with achieves further saving in the bit stream. Simulations carried out on SPOT images indicate the outperformance of the global compression package we performed.
The disease of osteoporosis shows itself both in a reduction of the bone mass and a degradation of the microarchitecture of the bone tissue. Radiological images of heel's bone are analyzed in order to extract informations about microarchitectural patterns. We first extract the gray-scale skeleton of the microstructures contained in the underlying images. More precisely, we apply the thinning procedure proposed by Mersal which preserves connectivity of the microarchitecture. Then, a post-processing of the resulting skeleton consists in detecting the points of intersection of the trabecular bones (multiple points). The modified skeleton can be considered as a powerful tool to extract discriminant features between Osteoporotic Patients (OP) and Control Patients (CP). For instance, computing the distance between two horizontal (respectively vertical) adjacent trabecular bones is a straightforward task once the multiple points are available. Statistical tests indicate that the proposed method is more suitable to discriminate between OP and CP than conventional methods based on binary skeleton.
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