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The problem of space debris is becoming more and more serious. It is necessary to monitor space debris normally. As an important technology of monitoring, optical observation needs to rely on fait image processing and astronomical positioning to obtain the RA-DEC position of space objects. Star map recognition is a key step in astronomical positioning, which directly affects the speed of image processing and the accuracy of results. In this paper, an improved fast star map recognition method based on fuzzy decision is proposed, which improves the accuracy of star map recognition while maintaining fast processing. This method is tested with the image of 25 cm telescope at Nanshan station in Xinjiang, and the results prove the effectiveness and stability of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes three different distribution strategies for very large 3D image deconvolution algorithms. The deconvolution problem is generic and tailored for spatio-spectral 3D image reconstruction. The three proposed algorithms for large-scale data are distributed in the sense that both the storage and the computations are distributed over several compute nodes. As a result, the workload is drastically reduced compared to a centralized approach where the storage and the computations are handled by a single compute node. The proposed algorithms are validated through experiments on simulated astronomical images. [ABSTRACT FROM AUTHOR]

Astronomical images from optical photometric surveys are typically contaminated with transient artifacts such as cosmic rays, satellite trails and scattered light. We have developed and tested an algorithm that removes these artifacts using a deep, artifact free, static sky coadd image built up through the median combination of point spread function (PSF) homogenized, overlapping single epoch images. Transient artifacts are detected and masked in each single epoch image through comparison with an artifact free, PSF-matched simulated image that is constructed using the PSF-corrected, model fitting catalog from the artifact free coadd image together with the position variable PSF model of the single epoch image. This approach works well not only for cleaning single epoch images with worse seeing than the PSF homogenized coadd, but also the traditionally much more challenging problem of cleaning single epoch images with better seeing. In addition to masking transient artifacts, we have developed an interpolation approach that uses the local PSF and performs well in removing artifacts whose widths are smaller than the PSF full width at half maximum, including cosmic rays, the peaks of saturated stars and bleed trails. We have tested this algorithm on Dark Energy Survey Science Verification data and present performance metrics. More generally, our algorithm can be applied to any survey which images the same part of the sky multiple times. [ABSTRACT FROM AUTHOR]

Critical crescent moon detection is a new subject in astronomical image processing. Astronomers take photos with valuable astronomical calculations using CCD cameras and telescopes. Crescent moon might not be visible in such photos, so they must be enhanced. For this purpose, we apply common image processing methods on crescent moon images such as power operator, gamma transformation, global and local histogram equalization, POSHE, CLAHE, local enhancement, gain/offset correction, homomorphic filtering, SSR, MSR, MSRCR, wavelet and curvelet transformations. Second, we use our new methods like PB, POSP, HShistV , CurvHShistV and local brightness intensity enhancement to detect the crescent. Then we evaluate and compare the outputs of these algorithms by standard subjective evaluation tests. Finally, we conclude that these methods can give us enhanced images in which the crescent moon can be seen by human observers. We introduce our new toolbox which is named CMD, a standalone tool developed in Matlab for Critical Crescent Moon Detection using the above techniques. [ABSTRACT FROM AUTHOR]

This paper presents an automated algorithm to extract dynamical features, such as stability constraints, from phase space maps. The functional representation of these constraints allows their inclusion in optimization problems and thus expands the use of dynamical tools in space mission design. The challenge to autonomously detect the regions of interest in stability maps is discussed through utilizing image processing algorithms to cluster map data. Additionally, to use the detected regions, both discrete and smooth functional representations are studied. Based on similar clustering techniques that have been considered in extracting and representing features of phase space maps, we proposed an adaptively map generation algorithm. It creates a nonuniform grid of points on a map which is denser near the boundaries of the regions of interest. Both representation and map generation algorithms provide significant performance enhancements in phase space analysis. All these techniques are illustrated on examples of stability maps in small body dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to the estimation of strongly varying backgrounds in astronomical images by means of small-objects removal and subsequent missing pixels interpolation. The method is based on the analysis of a pixel local neighbourhood and utilizes the morphological distance transform. In contrast to popular background-estimation techniques, our algorithm allows for accurate extraction of complex structures, like galaxies or nebulae. Moreover, it does not require multiple tuning parameters, since it relies on physical properties of CCD image sensors -- the gain and the readout noise characteristics. The comparison with other widely used background estimators revealed higher accuracy of the proposed technique. The superiority of the novel method is especially significant for the most challenging fluctuating backgrounds. The size of filtered-out objects is tunable; therefore, the algorithm may eliminate a wide range of foreground structures, including the dark current impulses, cosmic rays or even entire galaxies in deep field images. [ABSTRACT FROM AUTHOR]

This work devotes to the image deconvolution problem that restores clear image from its blurred and noisy measurements with little prior about the blur. A deconvolution method based on sparse and redundant representation theory is developed in this paper. It firstly represents the blur and image over different redundant dictionaries and imposes sparsity constraint to their representation coefficients respectively, then alternately estimates them using an iterative algorithm employing optimization technique. Experimental results on astronomical images show that the proposed method can achieve as good performance as the method requiring a known blur, which demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new linear filtering technique, the so-called matrix filters, that maximizes the signal-to-interference ratio of compact sources of unknown intensity embedded in a set of images by taking into account the cross-correlations between the different channels. By construction, the new filtering technique outperforms (or at least equals) the standard matched filter applied on individual images. An immediate application is the detection of extragalactic point sources in cosmic microwave background images obtained at different wavelengths. We test the new technique in two simulated cases: a simple two-channel case with ideal correlated color noise and more realistic simulations of the sky as it will be observed by the low-frequency instrument instrument of the upcoming ESA's Planck mission. In both cases, we observe an improvement with respect to the standard matched filter in terms of signal-to-noise interference, number of detections and number of false alarms. [ABSTRACT FROM PUBLISHER]

Three-dimensional (3-D) maps of the electron density and temperature in the solar atmosphere can be tomographically reconstructed from two-dimensional images that are measured by a variety of ground-based and space-based instruments. The electron density and temperature of the solar corona are fundamental parameters for understanding the physical mechanisms that contribute to space weather, or physical phenomena that can, in extreme cases, have adverse effects on Earth and the near-Earth space environment. New signal processing methods are required to take full advantage of the rich and complex suite of observations that are available from the current generation of Sun-observing spacecraft. In particular, this paper provides signal models and corresponding optimal state estimation methods for reconstructing the dynamic 3-D electron density and temperature structures in the solar corona with focus on the many challenges associated with solar tomography. [ABSTRACT FROM PUBLISHER]

We present methods used to measure the information in an astronomical image, in both a statistical and a deterministic way. We discuss the wavelet transform and noise modeling, and describe how to measure the information and the implications for object detection, filtering, and deconvolution. The perspectives opened up by the range of noise models, catering for a wide range of eventualities in physical science imagery and signals, and the new two-pronged but tightly coupled understanding of the concept of information have given rise to better quality results in applications such as noise filtering, deconvolution, compression, and object (feature) detection. We have illustrated some of these new results in this article. The theoretical foundations of our perspectives have been sketched out. The practical implications, too, are evident from the range of important signal processing problems which we can better address with this armoury of methods. The results described in this work are targeted at information and at relevance. While we have focused on experimental results in astronomical image and signal processing, the possibilities are apparent in many other application domains [ABSTRACT FROM PUBLISHER]

Reverse engineering offers a useful approach to reconstructing the processes through which contemporary, vintage, and antique images were produced. It virtually enables back-door access to other imagers' workshops, suggesting to the observant practitioner hitherto overlooked imaging techniques, as well as forensic reconstructions of the working procedures of past imagers. [ABSTRACT FROM AUTHOR]