Abstract-Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the Non Local means (NL means) algorithm introduced by Buades et al.[1], which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a datadriven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as Synthetic Aperture Radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case.

Jupiter is a complex and at the same time very powerful radio source in the decameter wavelength range. The emission is anisotropic, intrinsically variable at millisecond to hour timescales, and also modulated by various external processes at much longer periods, ranging from ∼10 h to months or years (including Jovian day and year, solar activity and solar wind variations, and for groundbased observations, terrestrial day and year). As a consequence, long-term observations and their statistical study have proved to be necessary for disentangling and understanding the observed phenomena. We have built a database from the available 26 yr of systematic, daily observations conducted at the Nançay Decameter Array and recorded in digital format. This database contains all observed Jovian decametric emissions, classified with respect to the time-frequency morphology, their dominant circular polarization, and maximum frequency. We present the results of the first statistical analysis of this database. We confirm the earlier classification of Jovian decameter emissions in Io-A, -A , -B, -C, -D and non-Io-A, -B, -C types, but we also introduce new emission types (Io-A and Io-B ) and precise and characterize the non-Io-D type. We determine the contours of all emission types in the CML−Φ Io plane (Central Meridian Longitude in Jupiter's System III coordinates versus Io Phase), provide representative examples of their typical time-frequency patterns, and the distribution of emission's maximum frequency as a function of Λ Io (Io's Longitude). Finally, we present a statistical analysis of the distributions of the occurrence rate, duration, intensity and polarization for each emission type. non-Io-DAM appears to be related to small-scale, possibly bursty auroral structures.

The Pierre Auger Observatory is exploring the potential of the radio detection technique to study extensive air showers induced by ultra-high energy cosmic rays. The Auger Engineering Radio Array (AERA) addresses both technological and scientific aspects of the radio technique. A first phase of AERA has been operating since September 2010 with detector stations observing radio signals at frequencies between 30 and 80 MHz. In this paper we present comparative studies to identify and optimize the antenna design for the final configuration of AERA consisting of 160 individual radio detector stations. The transient nature of the air shower signal requires a detailed description of the antenna sensor. As the ultra-wideband reception of pulses is not widely discussed in antenna literature, we review the relevant antenna characteristics and enhance theoretical considerations towards the impulse response of antennas including polarization effects and multiple signal reflections. On the basis of the vector effective length we study the transient response characteristics of three candidate antennas in the time domain. Observing the variation of the continuous galactic background intensity we rank the antennas with respect to the noise level added to the galactic signal.

Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches (so-called image-based) compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. Recent progress in natural image modeling also makes intensive use of patch comparison. A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared differences of intensities. For the case where noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature of image processing, detection theory and machine learning. By expressing patch (dis)similarity as a detection test under a given noise model, we introduce these criteria with a new one and discuss their properties. We then assess their performance for different tasks: patch discrimination, image denoising, stereo-matching and motion-tracking under gamma and Poisson noises. The proposed criterion based on the generalized likelihood ratio is shown to be both easy to derive and powerful in these diverse applications.