图象区域边界的参数和非参数统计检测法

噪声污染的图象,区域内灰度快速变化的图象,其图象区域边界检测通常用基于局部灰度统计的方法检测,常用的最大似然比检测法是基于图象各区域灰度服从正态分布假设的参数统计检验法,而图象边界的秩和检测法则是一种非参数统计检验法。本文对以上两种方法检测图象边界的性能进行分析,提出了一种改善检得边界质量的修正秩和算法,用三幅图象做了边界检测的对比研究。在此基础上,根据待处理图象的情况及检测边界的质量要求,提出了图象边界的不同检测方法合理选用的建议,     

基于边界和模糊集理论的三维图像分割

本文提出了一种基于边界和模糊集理论的三维图象分割方法，利用高斯－拉普拉斯算子检测出图象中各结构的边界后，通过区域增长获取感光趣结构的三维二值模板，由高斯－拉普拉斯图象和二值模板出发，利用模糊集理论，确定感兴趣结构的三维模糊模板，从而达到在体积图象中分割出感兴趣结构的目的。     

基于边缘检测的SAR图象分类融合技术

从基于象素的SAR图象分类和基于邻域的SAR图象分类特点出发,提出了一种基于边缘检测的SAR图象分类融合方法。该方法既保留了图象的边缘,避免了以往图象分类中出现的边缘模糊问题,又有效地对图象进行了区域划分。     

图象目标识别图形的自适应步级检测

建立图象目标识别模型，用形状、灰度和运动特征描述图象目标。基于目标建模，把目标识别、门限及目标图形区域步级检测、虚漏警调节和目标空域条件有机地结合起来，给出牵引式跟踪系统中图象目标识别图形的自适应步级检测算法。该算法用于检测具有识别特征的图象目标图形，并成功地应用到实际的实时跟踪系统中的图象目标识别和跟踪。对实际图象的处理结果和在实时跟踪系统中的实验说明本文研究的技术的有效性和适用性。     

新的运动图象检测器

本文根据CCITT所制定的标准提出了用于数字会议电视传输系统的运动图象检测方案,并研制了一种将运动图象的变化区域划分为运动区域和变动区域的检测器。通过计算机模拟对实际景物图象和标准序列图象进行检测,结果表明新的检测器性能良好。这种检测器对会议电视图象传输质量的提高和数字比特率的压缩将起到重要作用。     

用基函数神经网络实现多阈值图象分割

本文介绍了一种用基函数神经网络实现多阈值图象分割的新方法。它从函数逼近的角度研究基于灰度直方图的多阈值分割问题，提出了一种模糊反向传播学习算法，采用该算法的高斯基函数网络能够准确检测直方图中包含的子区域和它们的分布函数，而且速度很快。实验表明本文的方法在实际图象分割中是有效的。     

一种区域基图象编码新方法

本文提出了一种基于区域的图象编码新技术，它根据图象区域灰度分布特点，以灰度误差最小平方和为准则，采用四向递归二分法逐渐将图象表面划分为若干凸多边形，使之逼近原始图象．软件模拟实验表明，当头肩灰度图象压缩比超过20：1时，重建图象主观质量仍然较好．文中介绍了区域基和表面描述图象编码方法，着重论述了四向递归二分法的基本原理和算法，同时给出了若干实验结果。     

惯性平面下基于Gabor滤波和视网膜模型的目标检测

本文给出了一个在复杂背景下定位目标的方案。首先用欧几里德距离映射(EDM)算法和形态操作对特征惯性平面做骨架化操作，然后用Gabor滤波器组检测骨架图象中指定形状的区域，该形状的真实性则用改进的视网膜模型来验证。实验结果证明该方法是行之有效的。     

高分辨率图象实时数据采集及与微机接口

ＣＣＤ图象的解调测量法可以实现图象边缘点的高分辨率检测，达到高精度尺寸测量的目的。采用这种方法测量数据的产生速率由高频电子细分频率决定，数据量上被测量 图象中需检测的边缘点的多少决定。本文介绍了测量图象边缘间距或中心距ＣＣＤ图象解调测量法的数据采集、存贮和与微机接口方法。实现高精度、高速度、多边级点大数据量数据的实时有效采集及存贮，并可以和微机系统接口。     

基于小波变换的红外图象多尺度边缘检测

本文讨论了小波变换及其应用于多尺度图象边缘检测的原理，对一幅红外图象给出其多尺度边缘检测的计算机仿真结果，而且与传统的边缘检测方法进行比较，从而得出基于小波变换的多尺度边缘检测是一种较好的方法     

La compression d’images embarquée pour les missions spatiales

On-board space instruments produce more and more high amount of data due to the increase in both resolution and number of bits per pixel, not compensated by the reduced swath. Due to the stringent limitations (in terms of mass and power) which apply to on-board equipment, it is essential to reduce to a minimum the on-board sto-rage capacity and the on-board transmission rate needed to fullfill the mission: on-board image compression is a very powerful tool to optimise the resources. The availability, for spaceborne applications, of highly integrated circuits allows to implement on board very sophisticated real-time compression schemes and then to improve the performances of on-board compression. The whole image chain (from the sensor to the on-ground post-processing such as déconvolution and denoising) has to be considered in order to select the compression algorithm and the compression ratio. This article presents the compression algorithms, selected or currently studied, for space missions, and the methods of image quality evaluation.     

用于无线内窥镜系统的高效、低复杂度的准无损和无损压缩算法

为了降低无线内窥镜系统中无线通信的带宽以及无线发射的功耗,该文提出了一种高效、低复杂度的基于类似BAYER 彩色图像阵列的数字图像无损和准无损压缩/解压缩算法。通过对标准图像库中的7幅图像进行压缩的结果表明,该文提出的压缩算法比常规先插值后压缩的算法以及先压缩后插值的算法均具有更高的压缩性能和更低的复杂度;可实现对指定ROI区域实现无损压缩,其它区域实现准无损压缩。算法对无线内窥镜图像进行压缩时,可以获得平均图像压缩码率2.18bit/pixel,且PSNR大于47.57dB。     

A lossless image compression algorithm using variable block sizesegmentation

The redundancy in digital image representation can be classified into two categories: local and global. In this paper, we present an analysis of two image characteristics that give rise to local and global redundancy in image representation. Based on this study, we propose a lossless image compression scheme that exploits redundancy both at local and global levels in order to obtain maximum compression efficiency. The proposed algorithm segments the image into variable size blocks and encodes them depending on the characteristics exhibited by the pixels within the block. The proposed algorithm is implemented in software and its performance is better than other lossless compression schemes such as the Huffman, the arithmetic, the Lempel-Ziv and the JPEG.     

Low-complexity compression of multispectral images based on classified transform coding

Compression of remote-sensing images can be necessary in various stages of the image life, and especially on-board a satellite before transmission to the ground station. Although on-board CPU power is quite limited, it is now possible to implement sophisticated real-time compression techniques, provided that complexity constraints are taken into account at design time. In this paper we consider the class-based multispectral image coder originally proposed in [Gelli and Poggi, Compression of multispectral images by spectral classification and transform coding, IEEE Trans. Image Process. (April 1999) 476–489 [5]] and modify it to allow its use in real time with limited hardware resources. Experiments carried out on several multispectral images show that the resulting unsupervised coder has a fully acceptable complexity, and a rate–distortion performance which is superior to that of the original supervised coder, and comparable to that of the best coders known in the literature.     

结构信息最优的静止图像压缩算法研究

JPEG2000是基于小波变换的新一代静止图像压缩标准,与以往的压缩标准相比,其具有很多优点。但是JPEG2000以MSE作为图像失真评价标准,而MSE不能很好的符合人眼主观评分,进而很大地影响了JPEG2000的压缩性能。该文在JPEG2000标准框架下,提出了以结构相似度作为失真评价标准的静止图像压缩算法(SJPEG2000)。该算法以系数对图像结构信息贡献量的大小作为准则来截取码流,使压缩后的图像尽量保存原图像的结构信息。实验结果表明,该算法压缩得到的图像很好地保留了图像结构信息,压缩图像的主观质量得到提高,结构相似度值较原JPEG2000也有一定提高。     

No-reference quality assessment using natural scene statistics: JPEG2000.

Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a "reference" or "perfect" image. The obvious limitation of this approach is that the reference image or video may not be available to the QA algorithm. The field of blind, or no-reference, QA, in which image quality is predicted without the reference image or video, has been largely unexplored, with algorithms focusing mostly on measuring the blocking artifacts. Emerging image and video compression technologies can avoid the dreaded blocking artifact by using various mechanisms, but they introduce other types of distortions, specifically blurring and ringing. In this paper, we propose to use natural scene statistics (NSS) to blindly measure the quality of images compressed by JPEG2000 (or any other wavelet based) image coder. We claim that natural scenes contain nonlinear dependencies that are disturbed by the compression process, and that this disturbance can be quantified and related to human perceptions of quality. We train and test our algorithm with data from human subjects, and show that reasonably comprehensive NSS models can help us in making blind, but accurate, predictions of quality. Our algorithm performs close to the limit imposed on useful prediction by the variability between human subjects.     

基于小波的感兴趣区域无人机侦察图像的压缩

由于无人机飞行高度高、速度快,所以无人机侦察图像的目标和细节多、数据量大。为了实现图像数据的高速实时传输,需要对无人机侦察图像进行高压缩比压缩;但是在实际应用中,往往只对某些细节和目标感兴趣,要求这些区域清晰可见,这与图像的高压缩比压缩相矛盾,因此,本文提出了一种基于小波的无人机侦察图像压缩方法。该方法将感兴趣的军事目标从原图像中分离开来,对ROI（感兴趣区域）采用低压缩比甚至无损压缩,而对BG（背景区域）采用高压缩比压缩,最后合成两幅图像,解决了无人侦察机图像高压缩比和高质量之间的矛盾。     

Dictionary based surveillance image compression

Common image compression techniques suitable for general purpose may be less effective for such specific applications as video surveillance. Since a stationed surveillance camera always targets at a fixed scene, its captured images exhibit high consistency in content or structure. In this paper, we propose a surveillance image compression technique via dictionary learning to fully exploit the constant characteristics of a target scene. This method transforms images over sparsely tailored over-complete dictionaries learned directly from image samples rather than a fixed one, and thus can approximate an image with fewer coefficients. A set of dictionaries trained off-line is applied for sparse representation. An adaptive image blocking method is developed so that the encoder can represent an image in a texture-aware way. Experimental results show that the proposed algorithm significantly outperforms JPEG and JPEG 2000 in terms of both quality of reconstructed images and compression ratio as well.     

最大误差可控的高光谱图像聚类压缩算法

针对原有基于奇异值分解的最大误差可控的高光谱图像压缩(EC-SVD)算法未充分利用图像光谱矢量间冗余的问题,该文将高光谱图像压缩与聚类结合,提出最大误差可控的高光谱图像聚类压缩算法。分析发现,图像的光谱矢量间相似度越高越有利于得到好的最终压缩效果。因此,算法首先使用K-均值聚类对高光谱图像像元按光谱矢量聚类,以提高同类光谱矢量间的相似度;其次,对每一类像元分别使用EC-SVD算法思想压缩以控制最大误差。论文证明了当高光谱图像的像元个数与波段数之比较大,且聚类类数不大于8时,聚类能够提高图像最终压缩比。最后,设计整体压缩实验仿真流程,并对实际高光谱图像进行数值仿真。结果表明,在相同参数条件下,该文算法比EC-SVD算法得到的压缩比和信噪比均有提高,最大压缩比提高了10% 左右。该文算法能够有效提高EC-SVD算法的图像压缩效果。     

Speed-up fractal image compression with a fuzzy classifier

This paper presents a fractal image compression scheme incorporated with a fuzzy classifier that is optimized by a genetic algorithm. The fractal image compression scheme requires to find matching range blocks to domain blocks from all the possible division of an image into subblocks. With suitable classification of the subblocks by a fuzzy classifier we can reduce the search time for this matching process so as to speedup the encoding process in the scheme. Implementation results show that by introducing three image classes and using fuzzy classifier optimized by a genetic algorithm the encoding process can be speedup by about 40% of an unclassified encoding system.