Multichannel Blind Deconvolution As A Tool For Solar Image Restoration

A principle of restoration methods based on multichannel blind deconvolution (MBD) is introduced. The methods assume that for every un-degraded unobservable image several degraded observed images are available. It is better conditioned than classical single channel approach. The first algorithm represents a generalization of iterative deconvolution scheme introduced for single images. The second MBD algorithm is based on so-called subspace technique. The subspace method is not iterative and this possibly implies an implementation that can be computationally more efficient. Both methods are presented in applications to artificial image data (computer-generated multichannel degraded data) with known ideal image to get a comparison with restored one. Performance in a real situation on solar photosphere images is shown.     

Non-linear filtering of compression blocking effects in reconstructed images

The increasing use of data compression by space mission experiments poses the question of quality of the images obtained after the compression-decompression process. Indeed, working on an Image Compression Module (ICM), Using Discrete Cosine Transform (DCT), with 8*8 pixel-sized sub-images (each pixel being coded on eight bits), one can find blocking effects on their boundaries. Avril and Nguyen (1992, thereafter ANG 1992), have shown that One Neighbour Accounting Filters, used after image reconstruction without modifying the coding method , provide the best and fastest correction as far as linear filtering is concerned. We present here a non-linear method, also used after image reconstruction, but working on spatial frequencies. It allows us to segregate, in the Fourier space, the signal from the defect, and then to remove it through applying a filter adapted to the frequency spectrum of each spoiled image. Employing the reverse Fourier transform, we then retrieve the corrected image. The efficiency of this new method was tested by three different means:- when Fourier filtering is applied to a reference set of aerial photographs of the Earth, blocking effects are quite indistinguishable by human vision, even when zooming on the images, which was not the case with ONAF;- the improvement of the Root Mean Square (RMS) Error, calculated between the filtered and original images, is at least three times greater than the one obtained with ONAF;- the reconstruction of a three-dimensional view of a landscape, thanks to two stereoscopic images having undergone a compression-decompression process with an algorithm using DCT and a compression rate of about 10, is possible only after Fourier filtering has been applied.The quite good preliminary results of the application of Fourier filtering to the Clementine images of the Moon are also represented.     

Fast compression and reconstruction of astronomical images based on compressed sensing

With the fast increase in the resolution of astronomical images, the question of how to process and transfer such large images has become a key issue in astronomy. We propose a new real-time compression and fast reconstruction algorithm for astronomical images based on compressive sensing techniques. We first reconstruct the original signal with fewer measurements, according to its compressibility. Then,based on the characteristics of astronomical images, we apply Daubechies orthogonal wavelets to obtain a sparse representation. A matrix representing a random Fourier ensemble is used to obtain a sparse representation in a lower dimensional space. For reconstructing the image, we propose a novel minimum total variation with block adaptive sensing to balance the accuracy and computation time. Our experimental results show that the proposed algorithm can efficiently reconstruct colorful astronomical images with high resolution and improve the applicability of compressed sensing.     

Zeeman-Doppler imaging of late-type stars: The surface magnetic field of II Peg

Late-type stars in general possess complicated magnetic surface fields which makes their detection and in particular their modeling and reconstruction challenging. In this work we present a new Zeeman-Doppler imaging code which is especially designed for the application to late-type stars. This code uses a new multi-line cross-correlation technique by means of a principal component analysis to extract and enhance the quality of individual polarized line profiles. It implements the full polarized radiative transfer equation and uses an inversion strategy that can incorporate prior knowledge based on solar analogies. Moreover, our code utilizes a new regularization scheme which is based on local maximum entropy to allow a more appropriate reproduction of complex surface fields as those expected for late-type stars. In a first application we present Zeeman-Doppler images of II Pegasi which reveal a surprisingly large scale surface structure with one predominant (unipolar) magnetic longitude which is mainly radially oriented. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Astronomical image data compression by morphological skeleton transformation

Astronomical instruments currently provide a large amount of data. Nowadays, a large part of these data are image frames obtained with receivers of increasing size. The scan of large astronomical plates using fast microdensitometers gives image frames of over 30000×30000 pixels. More and more often, images are transmitted over a network in order to control the observations, to process the data, and to examine or to fill a data bank. The time taken for archiving, the cost of communication, the available memory given by magnetic tapes, and the limited bandwidth of transmission lines are reasons which lead us to examine the data compression of astronomical images.The astronomical image has the characteristic of being a set of astronomical sources in the sky background whose values are not zero. We are, in fact, only interested in the astronomical sources. Once a suitable detection is made, we generally want a compression without any distorsion. In this paper, we present a method which can be adapted for this purpose. It is based on morphological skeleton transformations. The experimental results show that it can give us an efficient compression. Moreover, the flexibility of choosing a structure element adapted to different images and the simplicity of implementation are other advantages of this method. Because of these characteristics, different compression applications may be treated.     

Poisson Recoding Of Solar Images For Enhanced Compression

The amount of useful scientific data that a space-borne telescope produces is often limited by the available telemetry of the platform. General purpose image compression schemes are usually used to compress the image either lossy or losslessly. These schemes do not take into account the fact that pixel values of typical solar images are only known to within a certain uncertainty range. We present a preprocessing method to enhance the performance (compression ratio) of any subsequent image compression scheme. The method uses estimates of the photon shot and thermal noises to compute a recoding look-up table that maps the initial data into uncertainty intervals. The recoding method is lossy in a mathematical sense but lossless in a physical sense, since the image alterations are guaranteed to be smaller than the Poisson noise. The performance of any further compression algorithm is enhanced while achieving a known guaranteed maximum absolute error for each pixel in the case of lossless compression. This revised version was published online in October 2005 with corrections to one of the authors' names.     

数字图象压缩方法在天文上应用的研究及实验

本文研究了数字图象压缩的方法、分类和原理以及该技术在天文领域应用的必要性。针对天文观测的特点和需要，经过分析比较，提出了天文图象压缩的可行性方案。通过应用计算机编程、压缩实验得到相应结果。文中对天文数字图象分别进行了以下压缩方法实验：无失真压缩中的哈夫曼编码和位平面编码；限失真压缩中的离散余弦变换编码及混合编码等。其压缩比分别可达到２—４．５（无失真压缩）和１０—３０（限失真压缩）。图像压缩所需时间随使用的计算机而异。文中详细列出各种压缩方法的实验结果。     

Astronomical Image Co-adding Method based on Wide-field Image Deconvolution

Due to the atmospheric turbulence, the static aberration, tracking and pointing errors of telescopes, the point spread functions (PSFs) in different fields of view are different. Meanwhile, there are different PSFs in the images obtained by different telescopes. The quality of co-adding image is limited by the image with the poorest quality, and finally the resolution and sensitivity of the quad-channel telescope will also be affected. Dividing the image into some regions with the same type of PSF, and deconvolving these regions can improve the quality of the co-adding image. According to this theory, an image restoration algorithm based on the PSF clustering is proposed. Firstly, this paper makes the PSF clustering analysis by using Self-Organizing Maps, and makes the image segmentation based on the result of the PSF clustering analysis, then using the clustered PSFs to make deconvolutions on the sub-images. Then, the restored sub-images after deconvolution are joined together. Finally, by through the image registration and co-adding, the image with a high signal to noise ratio can be obtained. The result shows that the signal to noise ratio of the astronomical images are improved with our method, and the detection capability on faint stars is also improved.     

一种固定选图数的实时幸运成像算法

幸运成像技术是一种从大量短曝光图像中选取少量幸运好图进行配准、叠加的高分辨率图像恢复技术,能够有效减小大气湍流对图像质量的影响,但传统的基于中央处理器(Central Processing Unit,CPU)的幸运成像算法难以实现实时化.利用现场可编程门阵列(Field Programmable Gate Array,...     

Automated morphological classification of galaxies based on projection gradient nonnegative matrix factorization algorithm

The development of automated morphological classification schemes can successfully distinguish between morphological types of galaxies and can be used for studies of the formation and subsequent evolution of galaxies in our universe. In this paper, we present a new automated machine supervised learning astronomical classification scheme based on the Nonnegative Matrix Factorization algorithm. This scheme is making distinctions between all types roughly corresponding to Hubble types such as elliptical, lenticulars, spiral, and irregular galaxies. The proposed algorithm is performed on two examples with different number of image (small dataset contains 110 image and large dataset contains 700 images). The experimental results show that galaxy images from EFIGI catalog can be classified automatically with an accuracy of ～93% for small and ～92% for large number. These results are in good agreement when compared with the visual classifications.     

Adaptive Optics Image Restoration Based on Frame Selection and Multi-frame Blind Deconvolution

Restricted by the observational condition and the hardware, adaptive optics can only make a partial correction of the optical images blurred by atmospheric turbulence. A postprocessing method based on frame selection and multi-frame blind deconvolution is proposed for the restoration of high-resolution adaptive optics images. By frame selection we mean we first make a selection of the degraded (blurred) images for participation in the iterative blind deconvolution calculation, with no need of any a priori knowledge, and with only a positivity constraint. This method has been applied to the restoration of some stellar images observed by the 61-element adaptive optics system installed on the Yunnan Observatory 1.2m telescope. The experimental results indicate that this method can effectively compensate for the residual errors of the adaptive optics system on the image, and the restored image can reach the diffraction-limited quality.     

A Multiscale Vision Model to Analyse Field Astronomical Images

We have implemented a multiscale vision model based on the wavelet transform to analyse field astronomical images. The discrete transform is performed by the à trous algorithm. The vision model is based on the notion of significant structures. We identify the pixels of the associated wavelet transform space (WTS) with the objects. For each scale a region labelling is carried out. An interscale connectivity graph is then established. In accordance with some rules that permit false detections to be removed, the objects and their sub-objects are identified. They define respectively trees and sub-trees in the graph. In this way, the identification of the WTS pixels of the tree related to a given object leads to the reconstruction of its image by the conjugate gradient method. The model has been tested successfully on simulated images of stars and galaxies which allow us to show the capabilities of the detection and restoration procedures of the model. Finally, tests on real images show that one can analyse complex structures better than with classical astronomical vision models.     

The influence of quark anomalous magnetic moment in the Nambu–Jona-Lasinio model with different regularizations

The Nambu–Jona-Lasinio model is known for its simplicity and capacity to reproduce some of the basic characteristics of the quantum chromodynamics phase diagram. However, since it is a nonrenormalizable model, there are regularization issues that should be treated conveniently. This is the case when considering the quark anomalous magnetic moment (AMM) when external constant magnetic fields are present. Regularization procedures based on entangled functions between the magnetic field and the cutoff of the model can predict first-order phase transitions for chiral symmetry restoration at finite values of magnetic fields and inverse magnetic catalysis. The strengths of magnetic fields explored in NJL model and lattice QCD do not show first-order phase transition. In the present work, we show that some of the previous results are regularization-dependent effects and how to handle the divergences using the vacuum magnetic regularization scheme.     

Morphological Classification of Galaxies using Computer Vision and Artificial Neural Networks: A Computational Scheme

The morphology of galaxies is an important issue in the large scale study of the Universe. The Hubble Deep Field project has already shown that the Universe contains billions and billions of galaxies. The Sloan Digital Sky Survey is expected to map the sky for one million galaxies. One of the major challenges facing astronomers today is how to automatically identify and classify large number of galaxies that will began to show up in the hundreds of thousands of digitized images from sky surveys. Today it is possible to address this problem with the help of advances occurring in computer vision and artificial neural networks technology. This paper describes a computational scheme to develop an automatic galaxy classifier. From the scheme it is possible to visualize several different types of automatic galaxy classifiers. Two types are presented here with prototype models. The first type uses the geometric shape features as the basis for classification. The second uses the direct pixel images of galaxies and artificial neural networks to do the classification. The results show that geometric shape features are very good indicators of different types of nearby galaxies. Three test cases were presented to the prototype geometric shape classifier and it was able to successfully classify all three of them. The direct image based neural network classifier was able to learn 97% of the 171 training patterns presented to it. However when the network was presented a test set of 37 independent patterns, it was only able to classify 57% percent of the test cases. This study demonstrates that a very robust and efficient automated galaxy classifier based on shape features and artificial neural network can be develop. This revised version was published online in July 2006 with corrections to the Cover Date.     

A two-dimensional Kolmogorov–Smirnov test for crowded field source detection: ROSAT sources in NGC 6397

We present a two-dimensional version of the classical one-dimensional Kolmogorov–Smirnov (KS) test, extending an earlier idea due to Peacock and an implementation proposed by Fasano and Franceschini. The two-dimensional KS test is used to optimize the goodness of fit in an iterative source-detection scheme for astronomical images. The method is applied to a ROSAT /HRI X-ray image of the post-core-collapse globular cluster NGC 6397 to determine the most probable source distribution in the cluster core. Comparisons to other widely used source-detection methods, and to a Chandra image of the same field, show that our iteration scheme is superior in measuring statistics-limited sources in severely crowded fields.     

天文图象数据无失真实时数据压缩方法

本文介绍一种用于天文观测图象数据无信息丢失的现场实时数据压缩方法。在对原始图象数据可压缩性统计分析之后，介绍了一种适合天文观测数据现场实时数据压缩的方法＂基础比特＋溢出比特＂编码方法。讨论了为提高压缩比而采取的各种措施。以ＤＥＮＩＳ项目中现场观测原始数据压缩为例，说明了信息保存型实时数据压缩的实现过程，最后给出了该方法的实验结果，实验表明，本文介绍的数据压缩方法在无任何信息丢失的情况下，可获得接近理论值的数据压缩比。     

Submillimetre observations of luminous z>4 radio-quiet quasars and the contribution of AGN to the submm source population

We present polarization observations of the gravitational lens system B1422+231 made at 8.4 GHz using the VLBA and the 100-m telescope at Effelsberg. All four images of the quasar show structure on the milliarcsec scale. The three bright images show tangential stretching as expected from lens models. Some basic properties of gravitational lensing are exhibited by this system. The surface brightness of images A and B are the same and the parity reversal expected in image B is revealed, for the first time, by polarization observations. There is a large differential Faraday rotation between images A and B.     

基于亚像素图像平移叠加提高大倾角同步卫星信噪比的方法研究

大倾角地球同步轨道(GEO)卫星相对地面并非完全静止,其星下点做南北方向的"8"字周期运动,倾角越大运动范围越大.这会降低地面望远镜对其进行凝视观测时的有效曝光时间,无法获得较高的信噪比.对地球同步轨道的监测中,在硬件条件不变的前提下,提出亚像素图像平移叠加方法.根据目标的运动速度和相邻帧图像的时间间隔,在亚像素尺度平移并对齐多幅图像,使GEO目标星象在图像序列中的位置重合,通过叠加多幅序列图像来提高目标信噪比,从而达到提升整个系统探测能力的目的.实测图像叠加结果表明,该方法可以显著提高该类目标的信噪比.叠加5幅图像时,整数像素叠加图像的信噪比约为原图像的1.7倍,而亚像素叠加图像的信噪比是原图像的2倍左右.     

A new segmentation algorithm for lunar surface terrain based on CCD images

Terrain classification is one of the critical steps used in lunar geomorphologic analysis and landing site selection. Most of the published works have focused on a Digital Elevation Model(DEM) to distinguish different regions of lunar terrain.This paper presents an algorithm that can be applied to lunar CCD images by blocking and clustering according to image features, which can accurately distinguish between lunar highland and lunar mare. The new algorithm, compared with the traditional algorithm, can improve classification accuracy. The new algorithm incorporates two new features and one Tamura texture feature. The new features are generating an enhanced image histogram and modeling the properties of light reflection, which can represent the geological characteristics based on CCD gray level images. These features are applied to identify texture in order to perform image clustering and segmentation by a weighted Euclidean distance to distinguish between lunar mare and lunar highlands.The new algorithm has been tested on Chang'e-1 CCD data and the testing result has been compared with geological data published by the U.S. Geological Survey. The result has shown that the algorithm can effectively distinguish the lunar mare from highlands in CCD images. The overall accuracy of the proposed algorithm is satisfactory, and the Kappa coefficient is 0.802, which is higher than the result of combining the DEM with CCD images.     

Research on Improving SNR of Large Angle Synchronous Satellite Based on Sub-pixel Translation and Superposition

Geosynchronous satellite with big inclination is not completely stationary relative to the ground. Its sub-satellite point moves periodically in the north-south direction with ground track of shape like “8”. The larger the inclination angle is, the larger the range of motion is. Such effect makes the effective exposure time of Geosynchronous satellite limited when observe it by optical telescope with stare mode, and the SNR (Signal Noise Ratio) of Geosynchronous satellite image could not be increased with long exposure time. In the monitoring of Geosynchronous orbit without changing hardware, a method called sub-pixel translating superposition of successive frames of images is proposed. By translating and aligning images in the sub-pixel scale according to the moving speed of the object and the time interval of the adjacent frame images, the positions of the Geosynchronous satellite in the image sequences are coincident, and then by superposition of these translational images, the SNR of moving Geosynchronous objects and the detection capability of the whole system can be improved. Results of superposition of real observed images show that the method can remarkably improve the SNR of moving. When five images are superimposed, compared with the SNR of the original image, the SNR of the superimposed image by integer pixels is increased by around 1.7 times, and the SNR of the superimposed image by sub-pixel is increased by about 2 times.