基于形态学算子的分形图像编码

本文提出了一种基于数学形态学算子的分形图像编码方法。首先用ＡＭＳＳ算子对原图像进行作用，得到的结果作为Ｄｏｍａｉｎ Ｐｏｏｌ。在此基础上，再对图像进行分割、搜索并获得仿射变换族，形成相应的ＰＩＦＳ。本文对该方法下的压缩变换、解码算法、Ｃｏｌｌａｇｅ定理等相关的理论进行了讨论。在编码过程中，采用ＡＭＳＳ图像作为Ｄｏｍａｉｎ Ｐｏｏｌ的意义在于，ＡＭＳＳ算子拓宽了Ｄｏｍａｉｎ Ｐｏｏｌ的边缘范围，使经     

图像编码压缩的分形基技术

本文介绍了图像编码与压缩的分形基方法，并且提供了实现的实用技术。就地理卫星遥感图像、雷达回波、卫星云图以及边界层大气湍流资料进行模拟试验。结果表明，压缩比达到１０－１９，平均相对误差为１０－３－１０－２级，重构图像能保持原图像特征，具有好的视觉效果。该方法适用于各类数字化遥感图像和其它气象资料的处理。在地学、生物、医学以及通讯工程中具有广阔应用前景。     

一种分形图像编码的新方法

由于分形图像编码过程非常费时，因此在前人的基础上，通过分析影响分形图像编码速度的相关因素，提出了使用图像块的改进方差来提高分形压缩性能的思想。在证明迭代函数系统不会改变图像块的改进方差的基础上，给出了基于改进方差的分形图像压缩方法。实验结果说明该方法在保证快速性的同时，重建图像的质量又得到了进一步的提高。     

非线性机械设备系统的分形神经网络诊断方法

从分形原理出发，提出了非线性机械设备系统信号的分形计算维数的概念。将分形计算维数概念与多层感知器神经网络结合，建立了机械设备 的分形神经网络诊断方法。通过对滚动轴承系统实例诊断获得了较为准确的模式识别的结果。     

智能材料结构损伤的分形神经网络诊断方法

针对智能复合材料的损伤诊断问题提出了采用人工神经网络将材料结构表面上的裂纹与材料内部的应力变化相结合的诊断方法。光纤珐珀传感器的小体积和高精度使之很适合于埋置在复合材料内部感受材料内部应力变化。而材料结构表面的裂纹是其内部受损伤的外在表现 ,根据裂纹在结构表面上的分布特征用分形的方法把表面的裂纹量化 ,获得其分维值 ,和内部的应力变化一起作为特征值输入到神经网络 ,利用神经网络的非线性处理能力进行在线的材料损伤识别。在一块 3 5cm× 3 5cm的复合材料试件上的实验结果表明这一方法是可靠的、有效的 ,完全可以进行材料损伤的在线监测以及进一步的材料寿命预测     

基于分形理论的图像压缩算法

基于分形的图像压缩编码方法是一种全新的编码方法,它利用图像的自相似性及比例特性,通过消除图像的几何冗余度来实现图像数据的压缩,介绍与对比几种分形图像压缩的典型方法。     

嵌入式小波图像编码算法的研究

分析了嵌入式零树小波编码（EZW）算法原理和特点。讨论了两个基于EZW算法的改进算法，即多级树集合分裂算法（SPIHT），集合分裂嵌入块编码（SPECK）。最后，对这些算法原理进行了比较和讨论，说明了嵌入式图像编码的研究方向。     

IFS分形码的快速图像检索算法

图像经分形编码后产生IFS分形码,它可被用来进行图像检索操作。针对图像检索的特点,将分形码中的位置参数替换为相对距离与方向系数。定义了分形码间的距离以及图像间的分形码距离,并取出分形码距离最小的前门幅图像作为检索结果,由此提出了基于IFS分形码的快速图像检索算法。从时间复杂性上分析,利用本文算法所需的检索时间与值域块的个数有关。实验结果表明,相对缩放与旋转变化,算法对位移与亮度变化具有较强的稳定性,其分形码距离的均值仅为14.07和20．05;并可检索到具有一定相似性的图像,且类间与类内分形码距离约相差8,类内距离远小于类间距离。     

基于图像编码技术的网络信息传输安全性研究

在研究图像编解码理论和计算图像相似性参数的基础上，提出了基于图像编码技术的网络信息安全传输方法，通过在网络上传输经过编码的参照图像和相似性参数约定，达到图像通信保密的目的，保障了需要传输的图像在网络上的安全性．     

分形集逼近算法的效率分析

本文就分形集的逼近效率进行了探讨,对几个著名的分形集给出了数值实验的效率报告,对分形集逼近算法存在的问题和发展进行了分析。     

A new modified fast fractal image compression algorithm

Abstract

In this paper, a new fractal image compression algorithm is proposed, in which the time of the encoding process is considerably reduced. The algorithm exploits a domain pool reduction approach, along with the use of innovative predefined values for contrast scaling factor, S, instead of searching it across. Only the domain blocks with entropy greater than a threshold are considered to belong to the domain pool. The algorithm has been tested for some well-known images and the results have been compared with the state-of-the-art algorithms. The experiments show that our proposed algorithm has considerably lower encoding time than the other algorithms giving approximately the same quality for the encoded images.     

Fast image vector quantization using a modified competitive learning neural network approach

The basic goal of image compression through vector quantization (VQ) is to reduce the bit rate for transmission or data storage while maintaining an acceptable fidelity or image quality. The advantage of VQ image compression is its fast decompression by table lookup technique. However, the codebook supplied in advance may not handle the changing image statistics very well. The need for online codebook generation became apparent. The competitive learning neural network design has been used for vector quantization. However, its training time can be very long, and the number of output nodes is somewhat arbitrarily decided before the training starts. Our modified approach presents a fast codebook generation procedure by searching for an optimal number of output nodes evolutively. The results on two medical images show that this new approach reduces the training time considerably and still maintains good quality for recovered images. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 413–418, 1997     

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.     

采用小波与神经网络的高效图像数据压缩方案

通过构造特别的映射、整函数和BP神经网络,获得一套基于神经网络的无损数据压缩方案。由于该方案能压缩已被小波编码压缩过的数据,因此将其嵌套入一好的小波编码系统就可以获得一种基于小波与神经网络的高效图像数据压缩方案。实验证明,该高效方案对于Lenna图像的压缩比为43∶1, 并且恢复的图像有较好的视觉效果。     

Digital watermarking based on fractal image coding

Abstract

With advances in computer network and multimedia technology, digital media are rapidly proliferating, and thus the issue of copyright protection for electronic publishing is receiving great attention. To achieve the goal of copyright protection, the digital watermarks are used to identify the owner of a certain image, so as to prevent illegal copying. Digital watermarking is the technique that embeds an invisible signal including owner identification and copy control information into multimedia data such as audio, video, and images. A new digital watermark approach based on fractal image coding is proposed in this paper. We present a way to use the fractal code as a means of embedding a watermark into image. The proposed approach has been shown to be resistant to general attacks, like StirMark. Moreover, someone who owns the decryption key can simply extract the digital watermark from the watermarked image without resorting to the original image.     

基于脉冲耦合神经网络的红外图像增强

在分析了脉冲耦合神经网络的工作机理和行为特性后,指出可以利用神经元的点火-熄灭特性对图像进行增强.为了区分神经元的点火方式,提出一种根据链接矩阵判定神经元点火方式的方法,并利用自然点火和捕获点火建立了能使图像得到增强的非线性映射.文中对算法参数的设置及其对增强图像的影响做了详细地讨论,实验结果表明该算法不仅能使图像的对比度和亮度得到适当的增强,而且能够有效地抑制图像中的椒盐噪声,尤其适用于对比度和亮度都较低的红外图像.     

基于提升小波变换和分形维数的声纳图像识别

分形理论在图像的纹理识别中得到了广泛应用,由于分形维数不能反映图像的空间信息,容易造成误识别。针对该问题并结合声纳图像的特点,通过提升结构构造了Haar小波,并将提升小波变换同分形理论相结合,利用小波分解的多分辨率特点和分形维数的多尺度特性,提高图像的识别率。采用Levenberg-Marquardt(L-M)算法优化的BP神经网络对不同信噪比的声纳图像进行分类识别。实验结果表明,文中方法不论在识别率还是识别时间上均优于传统纹理识别方法。     

基于神经网络的红外图像实时去斑方法

在分析红外图像斑纹产生机理基础上,提出利用人眼视网膜自适应调节原理的去斑方法。去斑时,根据距离不同作用不同原则,对中心及其周围像素分别予以不同权重;将斑纹信息加入求解过程,能有效快速去斑;去斑后,采用基于阈值的直方图规定化方法,以弥补对比度可能降低情况。研制出的DSP去斑系统实现了实时去斑功能。     

Cervical image classification based on image segmentation preprocessing and a CapsNet network model

Cervical cancer is one of the most common gynecological malignancies, and when detected and treated at an early stage, the cure rate is almost 100%. Colposcopy can be used to diagnose cervical lesions by direct observation of the surface of the cervix using microscopic biopsy and pathological examination, which can improve the diagnosis rate and ensure that patients receive fast and effective treatment. Digital colposcopy and automatic image analysis can reduce the work burden on doctors, improve work efficiency, and help healthcare institutions to make better treatment decisions in underdeveloped areas. The present study used a deep-learning model to classify the images of cervical lesions. Clinicians could determine patient treatment based on the type of cervix, which greatly improved the diagnostic efficiency and accuracy. The present study was divided into two parts. First, convolutional neural networks were used to segment the lesions in the cervical images; and second, a neural network model similar to CapsNet was used to identify and classify the cervical images. Finally, the training set accuracy of our model was 99%, the test set accuracy was 80.1%, it obtained better results than other classification methods, and it realized rapid classification and prediction of mass image data.     

基于神经网络的侧向激光雷达信号去噪算法

针对侧向激光雷达应用于气溶胶探测领域时,雷达回波信号易受噪声影响这一问题,本文提出了一种基于神经网络的激光雷达信号去噪算法。该算法在卷积神经网络基础上融合残差学习法和批量标准化,引入了注意力机制,改进激活函数,提升了网络性能和学习效率。采用本文提出的方法对噪声进行预测,实现了信号和噪声的有效分离,提高了侧向激光雷达CCD图像的信噪比。实验结果表明,使用本文提出的去噪算法对侧向激光雷达CCD图像进行去噪,图像的峰值信噪比提高了约5 dB,信号相对误差减小至9.62%,本文提出的去噪算法优于小波变换、维纳滤波等去噪方法,验证了该方法的可行性和实用性。