We present a new method for component separation aimed at extracting Sunyaev–Zel'dovich (SZ) galaxy clusters from multifrequency maps of cosmic microwave background (CMB) experiments. This method is designed to recover non‐Gaussian, spatially localized and sparse signals. We first characterize the cluster non‐Gaussianity by studying it on simulated SZ maps. We then apply our estimator on simulated observations of the Planck and Atacama Cosmology Telescope (ACT) experiments. The method presented here outperforms multifrequency Wiener filtering, both in the reconstructed average intensity for given input and in the associated error. In the absence of point source contamination, this technique reconstructs the ACT (Planck) bright (big) cluster central y parameter with an intensity that is about 84 (43) per cent of the original input value. The associated error in the reconstruction is about 12 and 27 per cent for the 50 (12) ACT (Planck) clusters considered. For ACT, the error is dominated by beam smearing. In the Planck case, the error in the reconstruction is largely determined by the noise level: a noise reduction by a factor of 7 would imply almost perfect reconstruction and 10 per cent error for a large sample of clusters. We conclude that the selection function of Planck clusters will strongly depend on the noise properties in different sky regions, as well as the specific cluster extraction method assumed.

We present a new method for the reconstruction of Sunyaev-Zel'dovich (SZ) galaxy clusters in future SZ-survey experiments using multiband bolometer cameras such as Olimpo, APEX, or Planck. Our goal is to optimise SZ-Cluster extraction from our observed noisy maps. None of the algorithms used in the detection chain is tuned using prior knowledge of the SZ-Cluster signal, or other astrophysical sources (Optical Spectrum, Noise Covariance Matrix, or covariance of SZ Cluster wavelet coefficients). First, a blind separation of the different astrophysical components that contribute to the observations is conducted using an Independent Component Analysis (ICA) method. This is a new application of ICA to multichannel astrophysical data analysis. Then, a recent non linear filtering technique in the wavelet domain, based on multiscale entropy and the False Discovery Rate (FDR) method, is used to detect and reconstruct the galaxy clusters. We use the Source Extractor software to identify the detected clusters. The proposed method was applied on realistic simulations of observations that we produced as mixtures of synthetic maps of the four brightest light sources in the range 143 GHz to 600 GHz namely the Sunyaev-Zel'dovich effect, the Cosmic Microwave Background (CMB) anisotropies, the extragalactic InfraRed point sources and the Galactic Dust Emission. We also implemented a simple model of optics and noise to account for instrumental effects. Assuming nominal performance for the near future SZ-survey Olimpo, our detection chain recovers 25% of the cluster of mass larger than 10 14 M , with 90% purity. Our results are compared with those obtained with published algorithms. This new method has a high global detection efficiency in the high-purity/low completeness region, being however a blind algorithm (i.e. without using any prior assumptions on the data to be extracted).

In this paper, we define a similarity measure between images in the context of (indexing and) retrieval. We use the Kullback-Leibler (KL) divergence to compare sparse multiscale image representations. The KL divergence between parameterized marginal distributions of wavelet coefficients has already been used as a similarity measure between images. Here we use the Laplacian pyramid and consider the dependencies between coefficients by means of non parametric distributions of mixed intra/interscale and interchannel patches. To cope with the high-dimensionality of the resulting description space, we estimate the KL divergences in the k-th nearest neighbor (kNN) framework (instead of classical fixed size kernel methods). Query-by-example experiments show the accuracy and robustness of the method.Index Terms-Image retrieval, sparse wavelet description, intra/interscale dependency, Kullback-Leibler divergence, k-th nearest neighbors.

Cone-Beam Computerized Tomography (CBCT) and Positron Emission Tomography (PET) are two complementary medical imaging modalities providing respectively anatomic and metabolic information of a patient. In the context of public health, one must address the problem of dose reduction of the potentially harmful quantities related to each exam protocol : X-rays for CBCT and radiotracer for PET. Two demonstrators based on a technological breakthrough (acquisition devices work in photon-counting mode) have been developed and we investigate in this paper the two related tomographic reconstruction problems. We formulate separately the CBCT and the PET problems in two general frameworks that encompass the physics of the acquisition devices and the specific discretization of the object to reconstruct. These objects may be observed from a limited number of angles of views and we take into account the specificity of the Poisson noise. We propose various fast numerical schemes based on proximal methods to compute the solution of each problem. In particular, we show that primal-dual approaches are well suited in the PET case when considering non differentiable regularizations such as Total Variation. Experiments on numerical simulations and real data are in favor of the proposed algorithms when compared with the well-established methods.