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。     

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。     

数字图像压缩方式在天文上的应用

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给出相应的结果。     

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。     

用SAVQ技术实现太阳射电频谱的数据压缩

对自适应矢量量化（SAVQ）技术在太阳射电频谱数据压缩中的应用进行了有意义的讨论．给出一种压缩比和失真可调的SAVQ压缩方法,指出提高数据压缩比的有效方法之一为样本集成,同时介绍了用该技术对北京天文台太阳射电频谱仪上的两个波段（1．0GHz－2．0GHz和2．6GHz－3．8GHz）的观测数据进行压缩的实用效果,并与国际同行所用的ICON方式进行性能比较．最后展示了数据压缩技术及其在天文上的应用．     

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。     

具有泊松噪声分布的图象复原方法

本文以ＣＣＤ图象为技术背景，指出ＣＣＤ图象噪声主要将以与信号有关的泊松噪声形式存在。根据图象形成的模型及图象统计特性，提出了最大似然（ＭＩ）法，最大后验概率（ＭＡＰ）法及最大熵（ＭＥ）复原方法。针对ＭＡＰ法提出了具体算法和参数估计方法。并提出应用图象分割法来提高计算机复原速度，节省存储空间。     

FITS,BMP和SCR图像格式及相互转换

本文介绍了图象的ＦＩＴＳ格式、ＢＭＰ格式和ＳＣＲ格式。ＦＩＴＳ格式已经是天文界的通用格式，几乎所有的天文软件包都支持这一格式，而ＢＭＰ格式在ＰＣ计算机上有广泛的运用，有大量的ＰＣ软件支持ＢＭＰ图象的显示，处理和打印。     

小波变换及其在天文地球动力学中的应用

小波变换是近几年来国际上流行的一种新的数学分析方法，近年来在天文学和天文地球动力学领域中得到了广泛的应用，文中对连续小波的概况，原理，有关技术及其在天文地球动力学中的应用作了简单而系统的介绍。     

天文图象数据无失真实时数据压缩方法

本介绍一种用于天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     

A novel method of lossless compression for 2-D astronomical spectra images

A novel method of lossless compression for astronomical spectra images is proposed in this paper. Firstly, Integer Wavelet Transform is adopted to perform decorrelation of the data. Afterwards, Embedded Zero-tree Wavelet encoder is employed to describe the zero-tree structure of wavelet coefficients, and then the resulting stream put through Embedded Zero-tree Wavelet encoder can be transformed to character string including only five characters that is easily compressed by entropy coding. Finally, Arithmetic encoder is chosen as the entropy coder here. Groups of simulation data based on LAMOST and observation data from SDSS are used in the experiment to demonstrate the new method, and the experimental results are much better than those of GZIP and JPEG2000.     

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.     

The Digital Archive of the Photographic Images of the Córdoba Observatory Plates Collections

The astronomical events registered in photographic plates are unique. A remarkable interest in using old plates in many areas of the current astronomical research is widely recognized by the IAU. There are almost ten thousand plates in several collections at the Córdoba Observatory and most of them are nearly one century old. This photographic material is difficult to handle in order to get the information stored in it. The goal of this project is to make a digital archive of images of these collections. In this first survey plates are being scanned with a standard commercial transparency scanner. These low spatial resolution, low digital resolution and low signal-to-noise ratio images are intended for facilitating handling and visual inspection. However, one arc-second positions can be obtained. Photometric quality has not been yet determined. Otherwise high quality digital images can be acquired with a microdenstitometer based on a scientific grade CCD whose implementation is at its final stage. The possibility of using a better-performance scanner is being evaluated. It is planned to permit the access to low-resolution images via internet. High-resolution images of selected zones will be available upon request. At this first stage, collections totally digitized at low resolution are:Carte du Ciel and Small Magellanic Cloud Zones. Gaviola's Eta Carinae Spectra and Pulkovo Zones are still in process.     

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.     

A quantitative analysis of source detection approaches in optical, infrared, and radio astronomical images

A variety of software is used to solve the challenging task of detecting astronomical sources in wide field images. Additionally, computer vision methods based on well-known or innovative techniques are arising to face this purpose. In this paper, we review several of the most promising methods that have emerged during the last few years in the field of source detection. We specifically focus on methods that have been designed to deal with images with Gaussian noise distributions. The singularity of this analysis is that the different methods have been applied to a single dataset consisting of optical, infrared, and radio images. Thus, the different approaches are applied on a level playing field, and the results obtained can be used to evaluate and compare the methods in a meaningful, quantitative way. Moreover, we present the most important strengths and weaknesses of the methods for each type of image as well as an extensive discussion where the methods with best performances are highlighted.     

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.     

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.