We propose to apply a marked point process to detect a galaxy filament network. From the "quality candy" model, initially developed for road network extraction in remotely sensed images, we adapt the data term to the filament detection. The optimization is realized by a simulated annealing using a reversible jump Markov Chain Monte Carlo algorithm. Results are presented on a numerical simulation and on an astronomical survey

Nuclear medicine imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these imaging systems is Poisson noise. In this paper, we develop a novel multiscale image restoration procedure for photon imaging systems. It consists in separating in the wavelet-domain data points which belong to structures from those due to the noisy background. The latter are replaced by coefficients obtained by the introduction of a multiresolution regularity constraint. The restored image represents therefore a compromise between fidelity of the data and fidelity of the a priori knowledge on the smoothness of the solution. The performance of the method is assessed with simulated data experiments.

The analysis of SAR images requires to reduce the speckle noise due to the coherent character of the radar signal. This noise is multiplicative, which often leads to logarithmically transform the modulus. A bias, which depends on the local homogeneity is introduced, and the noise is amplified. The minimum variance estimator leads to process the energy image instead of the modulus one for reducing this multiplicative noise. The proposed methods are based on a multiscale vision model for which the image is only described by its significant structural features on a set of scales. The multiscale analysis is performed by a redundant discrete wavelet transform, the a trous algorithm. We have determined the distribution law of the wavelet coefficient of the energy in the case of a statistically uniform image. This allows us to extract the significant wavelet coefficients, according to the noise. A filtering algorithm is derived by taking into account only these coefficients. This method is extended to a co-addition of SAR images. Taking into account the multiresolution support obtained from the thresholding in the wavelet transform space (WTS), the image is decomposed into a set of objects, by a 3D segmentation in WTS.

Astronomical images currently provide large amounts of data. Lossy compression algorithms have recently been developed for high compression ratios. These compression techniques introduce distortion in the compressed images and for high compression ratios, a blocking effect appears. A new algorithm based on the regularization theory is proposed for the restoration of such lossy compressed astronomical images. The image is restored scale by scale in a multiresolution scheme and the information lost during the compression is recovered by applying a regularization constraint. The experimental results show that the blocking effect is reduced and some measurements made on a simulated image show that the astrometric and the photometric properties of the restored images are improved.

The large-scale distribution of galaxies in the Universe exhibits structures at various scales, these so-called groups, clusters, and superclusters of galaxies being more or less hierarchically organized. A specific vision model is needed in order to detect, describe, and classify each component of this hierarchy. To do so, we have developed a multiscale vision model based on an unfolding into a scale space allowing us to detect structures of different sizes. A discrete wavelet transform is done by the a trous algorithm. The algorithm is implemented for astronomical images and also for lists of object positions, currently called catalogues in astronomical literature. Some applications on astrophysical data of cosmological interest are briefly described: (1) inventory procedures for galaxy counts on wide-field images, (2) processing of X-ray cluster images lending to the analyses of the total matter distribution, and (3) detection of large-scale structures from galaxy counts, From the analyses of N-body simulations we show that the vision model from the wavelet transform provides a new statistical indicator on cosmological scenarios.

Describes a new method for the computation of a disparity map between a couple of stereo images. The disparities are computed along the x and y axes, respectively at each point of the image. In order to compute the disparity field, first a set of ground control points is detected in both images. Next, a mapping of the disparities over the entire image is done using the kriging method. Finally, the stereo couple of images is registered using the disparity maps.>

Presents a method to compress an image without any distortion by using a central prediction method hierarchically on controlling the entropy of results. This method permits one to minimize the entropy of an image.>