数字图像压缩方法在天文上的应用

概述了数字图像压缩技术在天文领域应用的必要性。针对天文观测的特点和研究的需要，经过研究、分析和比较，提出了天文图像压缩的可行方案。通过应用计算机编程及压缩实验并给出相应的结果。     

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.     

一种新的基于2维傅里叶谱图像的恒星光谱特征提取方法和深度网络分类应用

天体光谱分类是天文学研究的重要内容之一,其关键是从光谱数据中选择和提取对分类识别最有效的特征构建特征空间.提出一种新的基于2维傅里叶谱图像的特征提取方法,并应用于LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope)恒星光谱数据的分类研究中.光谱数据来源于LAMOST Data Release 5(DR5),选取30000条F、 G和K型星光谱数据,利用短时傅里叶变换(Short-Time Fourier Transform, STFT)将1维光谱数据变换成2维傅里叶谱图像,对得到的2维傅里叶谱图像采用深度卷积网络模型进行分类,得到的分类准确率是92.90%.实验结果表明通过对LAMOST恒星光谱数据进行STFT可得到光谱的2维傅里叶谱图像,谱图像构成了新的光谱数据特征和特征空间,新的特征对于光谱数据分类是有效的.此方法是对光谱分类的一种全新尝试,对海量天体光谱的分类和挖掘处理有一定的开创意义.     

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。     

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。     

分块DPCM与5/3整数小波变换结合的天文图像无损压缩

针对海量的天文图像数据与有限的存储空间和带宽资源之间矛盾日益突出这一问题,提出一种无损压缩方法,首先将超大天文图像分块,再使用差分脉冲编码调制和5/3整数小波变换,最后使用霍夫曼算法编码。对该方法的原理和具体实现做了详细的分析与介绍,通过实验验证该方法比天文中常用的tar、PKZip、WinZip、WinRar软件在压缩比上分别提高了30%、29%、26%、2%,压缩速度远大于WinZip和WinRar;且该算法实现简单,适合硬件实现和利于并行处理。     

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.     

A Regularized Image Restoration Algorithm for Lossy Compression in Astronomy

Astronomical images currently provide large amounts of data. Lossy compression algorithms have recently been developed for high compression ratios. These compression technique introduce distortion in the compressed images and for high compression ratios, a blocking effect appears. We propose a modified compression algorithm based on the hcompress scheme, and we introduce a new decompression method based on the regularization theory The image is restored scale by scale in a multiresolution scheme and the information lost during the compression is recovered by applying a Tikhonov regularization constraint. The experimental results show that the blocking effect is reduced and some measurements made on a simulated image show that the astrometric and the photometric properties of the restored images are improved.     

云南天文台新型斑点象探测系统

斑点成象技术能有效地消除地球大气湍流的不良影响,实现地基大型天文望远镜的衍射受限分辨率成象,其所需的原始数据是天文目标及参考星的一系列的短曝光斑点象,它们取自望远镜的终端设备：斑点象探测系统。文中对比该技术对原始数据的要求：介绍了云南天文台研制的新型斑点象探测系统的结构和性能。实际观测结果表明,该系统基本能满足要求。     

The Potsdam image processing software

The method of adaptive filtering as a useful tool for the smoothing and analysing of astronomical images is described in a heuristic way. The filter recognizes the local signal resolution (which usually varies strongly across the image) and adapts its own impulse response to this resolution. The advantages of this method are explained and some examples are given.     

Automatic Identification and Extraction of Clouds from Astronomical Images Based on Support Vector Machinetwo

For the time-domain astronomical research, the optical telescopes with a small and medium aperture can get a huge amount of data through automatic sky surveying. A certain proportion of automatically acquired data are interfered by clouds, which makes it very difficult to automatically extract the dim objects and make photometry. Therefore, it is necessary to identify and extract clouds from these images as the index figures for a reference in the subsequent information extraction. In this paper, an astronomical image selection system based on the support vector machine is proposed, which sets the gray value inconsistency and texture difference as the reference to select the images interfered by clouds. Based on the classification results, by through the histogram transformation and feature selection, the index figures of clouds can be further extracted. The experimental results show that our method can achieve the real-time selection of astronomical images with a classification accuracy better than 98%. By the histogram transformation and feature selection the index figure of clouds can be preliminarily extracted as the references for the photometry and dim object extraction.     

Unbiased image reconstruction as an inverse problem

An unbiased method for improving the resolution of astronomical images is presented. The strategy at the core of this method is to establish a linear transformation between the recorded image and an improved image at some desirable resolution. In order to establish this transformation only the actual point spread function and a desired point spread function need be known. No image actually recorded is used in establishing the linear transformation between the recorded and improved image.
This method has a number of advantages over other methods currently in use. It is not iterative, which means it is not necessary to impose any criteria, objective or otherwise, to stop the iterations. The method does not require an artificial separation of the image into 'smooth' and 'point-like' components, and thus is unbiased with respect to the character of structures present in the image. The method produces a linear transformation between the recorded image and the deconvolved image, and therefore the propagation of pixel-by-pixel flux error estimates into the deconvolved image is trivial. It is explicitly constrained to preserve photometry and should be robust against random errors.     

基于大视场反卷积的天文图像叠加方法

地基望远镜在成像过程中,由于受大气湍流、望远镜静态像差、跟踪误差、指向误差及视场变化的影响,不同视场区域的PSF (Point Spread Function)具有差异;同时,不同望远镜获取的图像PSF也存在差异.将多个望远镜获取的星象直接叠加至相同的区域后,图像质量受像质最差的望远镜限制,最终观测分辨率和灵敏度均会受到影响.通过图像复原,可以提高图像质量,进而提高叠加效果.根据该思路提出了1种基于PSF分区的迭代图像复原方法:该方法首先通过SOM (Self-organizing Maps)对PSF进行聚类分析,利用同类别PSF的平均PSF进行反卷积,再将反卷积结果按PSF聚类结果分割为不同大小的子图,最后将子图进行拼接.图像复原在提高图像质量的同时,降低了PSF不一致性对图像叠加带来的影响.将几个望远镜在同一时刻获取的图像经反卷积处理之后利用图像配准算法进行矫正并叠加,可获得高信噪比图像.对实际望远镜获取的数据处理后的结果表明:图像在进行复原和叠加过程中,星象目标信噪比不断提升,提高了成像系统对暗星的探测能力.     

A method to analyze imaging radio data on solar flares

Microwave imaging data are widely used in analysis of solar flares. To identify essential features and study their behavior, one has to deal with large data sets of hundreds of frames. This allows a single image, a variance map, to represent the overall dynamics of the event. A method is presented that allows investigation of the detected positions of sources in both Stokes I and V using the analysis of variance maps, combined difference images, and total flux time profiles.     

Interactive image retrieval in large astronomical archives: The Aspect system

We present a method to access efficiently and effectively images in large astronomical archives. This method allows users to interactively participate in the retrieval process. They may virtually 'navigate' through the archive for choosing images that are relevant for their purposes. The navigation process is performed by switching back and forth between three retrieval-system modes: a query mode, a browse mode and an inspection mode. The method has been applied to an image retrieval system to access solar radio spectrograms archived at ETH in Zurich. The system, called ASPECT, allows access to over 50000 data sets on-line.     

A novel hybrid algorithm for lucky imaging

Lucky imaging is a high-resolution astronomical image recovery technique with two classic implementation algorithms, i.e. image selecting, shifting and adding in image space, and data selecting and image synthesizing in Fourier space. This paper proposes a novel lucky imaging algorithm where with space-domain and frequency-domain selection rates as a link, the two classic algorithms are combined successfully, making each algorithm a proper subset of the novel hybrid algorithm. Experimental results show that with the same experiment dataset and platform, the high-resolution image obtained by the proposed algorithm is superior to that obtained by the two classic algorithms. This paper also proposes a new lucky image selection and storage scheme, which can greatly save computer memory and enable lucky imaging algorithm to be implemented in a common desktop or laptop with small memory and to process astronomical images with more frames and larger size. In addition, through simulation analysis,this paper discusses the binary star detection limits of the novel lucky imaging algorithm and traditional ones under different atmospheric conditions.     

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.     

Direct method of image and spectrum recovery in high-energy astronomy

In this paper we propose to use the method of iterative calculation to solve directly the imaging equation for image recovery. The same method can be applied to the recovery of one-dimensional spectra. During the iteration suitable constraints are applied to effect nonlinear control over the process. The use of fitting technique in the treatment of the imaging equation suppresses noise and raises sensitivity. Analyses of both Monte Carlo samples and actual high-energy astronomical data showed that the direct method is more sensitive and having greater resolving power than the traditional method, is capable of revealing simultaneously both diffuse and discrete structures in the target object and can be used on data of lower statistical quality. In principle, the direct method can be used in all types of image and spectrum recovery in other fields.     

CLEAN算法在天文图像空域重建中的应用

将CLEAN算法用于天文图像空域重建方法 :迭代位移叠加法中的消卷积过程 ,由于它同样是在空间域中进行的迭代减法过程 ,避免了必须把图像变换到傅里叶空间频率域中 ,然后用除法消卷积的传统做法 ,因而满足了仅在空间域中重建目标像的需要 ,使迭代位移叠加法更加简捷。用该方法对天文目标进行的观测实验得到了很好的像复原结果。     

Sub-image data processing in Astro-WISE

Most often, astronomers are interested in a source (e.g., moving, variable, or extreme in some colour index) that lies on a few pixels of an image. However, the classical approach in astronomical data processing is the processing of the entire image or set of images even when the sole source of interest may exist on only a few pixels of one or a few images. This is because pipelines have been written and designed for instruments with fixed detector properties (e.g., image size, calibration frames, overscan regions, etc.). Furthermore, all metadata and processing parameters are based on an instrument or a detector. Accordingly, out of many thousands of images for a survey, this can lead to unnecessary processing of data that is both time-consuming and wasteful. We describe the architecture and an implementation of sub-image processing in Astro-WISE. The architecture enables a user to select, retrieve and process only the relevant pixels in an image where the source exists. We show that lineage data collected during the processing and analysis of datasets can be reused to perform selective reprocessing (at sub-image level) on datasets while the remainder of the dataset is untouched, a difficult process to automate without lineage.