High contrast optical imaging of companions: the case of the brown dwarf binary HD-130948BC

High contrast imaging at optical wavelengths is limited by the modest correction of conventional near-IR optimized AO systems.We take advantage of new fast and low-readout-noise detectors to explore the potential of fast imaging coupled to post-processing techniques to detect faint companions to stars at small separations. We have focused on I-band direct imaging of the previously detected brown dwarf binary HD130948BC,attempting to spatially resolve the L2+L2 benchmark system. We used the Lucky-Imaging instrument FastCam at the 2.5-m Nordic Telescope to obtain quasi diffraction-limited images of HD130948 with ~0.1" resolution.In order to improve the detectability of the faint binary in the vicinity of a bright (I=5.19 \pm 0.03) solar-type star,we implemented a post-processing technique based on wavelet transform filtering of the image which allows us to strongly enhance the presence of point-like sources in regions where the primary halo dominates. We detect for the first time the BD binary HD130948BC in the optical band I with a SNR~9 at 2.561"\pm 0.007" (46.5 AU) from HD130948A and confirm in two independent dataset that the object is real,as opposed to time-varying residual speckles.We do not resolve the binary, which can be explained by astrometric results posterior to our observations that predict a separation below the NOT resolution.We reach at this distance a contrast of dI = 11.30 \pm 0.11, and estimate a combined magnitude for this binary to I = 16.49 \pm 0.11 and a I-J colour 3.29 \pm 0.13. At 1", we reach a detectability 10.5 mag fainter than the primary after image post-processing. We obtain on-sky validation of a technique based on speckle imaging and wavelet-transform processing,which improves the high contrast capabilities of speckle imaging.The I-J colour measured for the BD companion is slightly bluer, but still consistent with what typically found for L2 dwarfs(~3.4-3.6).Comment: accepted in A\&

Compressive Imaging via Approximate Message Passing with Image Denoising

We consider compressive imaging problems, where images are reconstructed from a reduced number of linear measurements. Our objective is to improve over existing compressive imaging algorithms in terms of both reconstruction error and runtime. To pursue our objective, we propose compressive imaging algorithms that employ the approximate message passing (AMP) framework. AMP is an iterative signal reconstruction algorithm that performs scalar denoising at each iteration; in order for AMP to reconstruct the original input signal well, a good denoiser must be used. We apply two wavelet based image denoisers within AMP. The first denoiser is the "amplitude-scaleinvariant Bayes estimator" (ABE), and the second is an adaptive Wiener filter; we call our AMP based algorithms for compressive imaging AMP-ABE and AMP-Wiener. Numerical results show that both AMP-ABE and AMP-Wiener significantly improve over the state of the art in terms of runtime. In terms of reconstruction quality, AMP-Wiener offers lower mean square error (MSE) than existing compressive imaging algorithms. In contrast, AMP-ABE has higher MSE, because ABE does not denoise as well as the adaptive Wiener filter.Comment: 15 pages; 2 tables; 7 figures; to appear in IEEE Trans. Signal Proces

Image blur estimation based on the average cone of ratio in the wavelet domain

In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio (ACR) histogram. The key properties of ACR are twofold: it is powerful in estimating local edge regularity, and it is nearly insensitive to noise. We use these properties to estimate the blurriness of the image, irrespective of the level of noise. In particular, the center of gravity of the ACR histogram is a blur metric. The method is applicable both in case where the reference image is available and when there is no reference. The results demonstrate a consistent performance of the proposed metric for a wide class of natural images and in a wide range of out of focus blurriness. Moreover, the proposed method shows a remarkable insensitivity to noise compared to other wavelet domain methods

Structures in Galaxy Clusters

The analysis of the presence of substructures in 16 well-sampled clusters of galaxies suggests a stimulating hypothesis: Clusters could be classified as unimodal or bimodal, on the basis of to the sub-clump distribution in the {\em 3-D} space of positions and velocities. The dynamic study of these clusters shows that their fundamental characteristics, in particular the virial masses, are not severely biased by the presence of subclustering if the system considered is bound.Comment: (16 pages in LATEX, 4 tables in LATEX are at the end of the file, the figures not included are available upon request), REF SISSA 158/93/