Logarithmic mathematical morphology: a new framework adaptive to illumination changes

A new set of mathematical morphology (MM) operators adaptive to illumination changes caused by variation of exposure time or light intensity is defined thanks to the Logarithmic Image Processing (LIP) model. This model based on the physics of acquisition is consistent with human vision. The fundamental operators, the logarithmic-dilation and the logarithmic-erosion, are defined with the LIP-addition of a structuring function. The combination of these two adjunct operators gives morphological filters, namely the logarithmic-opening and closing, useful for pattern recognition. The mathematical relation existing between ``classical'' dilation and erosion and their logarithmic-versions is established facilitating their implementation. Results on simulated and real images show that logarithmic-MM is more efficient on low-contrasted information than ``classical'' MM

Ceres' opposition effect observed by the Dawn framing camera

The surface reflectance of planetary regoliths may increase dramatically towards zero phase angle, a phenomenon known as the opposition effect (OE). Two physical processes that are thought to be the dominant contributors to the brightness surge are shadow hiding (SH) and coherent backscatter (CB). The occurrence of shadow hiding in planetary regoliths is self-evident, but it has proved difficult to unambiguously demonstrate CB from remote sensing observations. One prediction of CB theory is the wavelength dependence of the OE angular width. The Dawn spacecraft observed the OE on the surface of dwarf planet Ceres. We characterize the OE over the resolved surface, including the bright Cerealia Facula, and to find evidence for SH and/or CB. We analyze images of the Dawn framing camera by means of photometric modeling of the phase curve. We find that the OE of most of the investigated surface has very similar characteristics, with an enhancement factor of 1.4 and a FWHM of 3{\deg} (broad OE). A notable exception are the fresh ejecta of the Azacca crater, which display a very narrow brightness enhancement that is restricted to phase angles $< 0.5${\deg} (narrow OE); suggestively, this is in the range in which CB is thought to dominate. We do not find a wavelength dependence for the width of the broad OE, and lack the data to investigate the dependence for the narrow OE. The prediction of a wavelength-dependent CB width is rather ambiguous. The zero-phase observations allow us to determine Ceres' visible geometric albedo as $p_V = 0.094 \pm 0.005$. A comparison with other asteroids suggests that Ceres' broad OE is typical for an asteroid of its spectral type, with characteristics that are primarily linked to surface albedo. Our analysis suggests that CB may occur on the dark surface of Ceres in a highly localized fashion.Comment: Credit: Schr\"oder et al, A&A in press, 2018, reproduced with permission, \copyright ES

Logarithmic Image Processing for Color Images

43 pagesInternational audienceThe present paper is an extension to color images of the LIP (Logarithmic Image Processing) framework, initially dedicated to gray level image

Radio Observations of the January 20, 2005 X-Class Event

We present a multi-frequency and multi-instrument study of the 20 January 2005 event. We focus mainly on the complex radio signatures and their association with the active phenomena taking place: flares, CMEs, particle acceleration and magnetic restructuring. As a variety of energetic particle accelerators and sources of radio bursts are present, in the flare-ejecta combination, we investigate their relative importance in the progress of this event. The dynamic spectra of {Artemis-IV-Wind/Waves-Hiras with 2000 MHz-20 kHz frequency coverage, were used to track the evolution of the event from the low corona to the interplanetary space; these were supplemented with SXR, HXR and gamma-ray recordings. The observations were compared with the expected radio signatures and energetic-particle populations envisaged by the {Standard Flare--CME model and the reconnection outflow termination shock model. A proper combination of these mechanisms seems to provide an adequate model for the interpretation of the observational data.Comment: Accepted for publication in Solar Physic

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Region-enhanced passive radar imaging

The authors adapt and apply a recently-developed region-enhanced synthetic aperture radar (SAR) image reconstruction technique to the problem of passive radar imaging. One goal in passive radar imaging is to form images of aircraft using signals transmitted by commercial radio and television stations that are reflected from the objects of interest. This involves reconstructing an image from sparse samples of its Fourier transform. Owing to the sparse nature of the aperture, a conventional image formation approach based on direct Fourier transformation results in quite dramatic artefacts in the image, as compared with the case of active SAR imaging. The regionenhanced image formation method considered is based on an explicit mathematical model of the observation process; hence, information about the nature of the aperture is explicitly taken into account in image formation. Furthermore, this framework allows the incorporation of prior information or constraints about the scene being imaged, which makes it possible to compensate for the limitations of the sparse apertures involved in passive radar imaging. As a result, conventional imaging artefacts, such as sidelobes, can be alleviated. Experimental results using data based on electromagnetic simulations demonstrate that this is a promising strategy for passive radar imaging, exhibiting significant suppression of artefacts, preservation of imaged object features, and robustness to measurement noise