Wavelet‐based adaptive vector quantization for still‐image coding

Wavelet transform coding (WTC) with vector quantization (VQ) has been shown to be efficient in the application of image compression. An adaptive vector quantization coding scheme with the Gold‐Washing dynamic codebook‐refining mechanism in the wavelet domain, called symmetric wavelet transform‐based adaptive vector quantization (SWT‐GW‐AVQ), is proposed for still‐image coding in this article. The experimental results show that the GW codebook‐refining mechanism working in the wavelet domain rather than the spatial domain is very efficient, and the SWT‐GW‐AVQ coding scheme may improve the peak signal‐to‐noise ratio (PSNR) of the reconstructed images with a lower encoding time. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 166–174, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10024     

第二代曲波变换的图像降噪新算法

Curvelet变换表示曲线奇异函数的异向性及图像边缘时,具有比小波变换更优的表示特性。针对小波图像降噪存在的不足,分析基于wrapping算法的快速离散曲波变换的特点,提出结合循环平移、厄尔迭代方法和蒙特卡洛阈值规则的新消噪方法。该算法充分利用曲波系数的相关性,消除了因Curvelet变换缺乏平移不变性引起的图像＂划痕＂失真和＂振铃＂效应。实验结果表明,该算法与传统的小波消噪、二代小波消噪、小波包消噪和曲波硬阈值消噪相比,得到降噪图像的峰值信噪比更高,视觉效果更好。     

扩展方向树的遥感图像小波包压缩算法

针对遥感图像纹理丰富、空间相关性弱,普通压缩算法容易造成高频信息丢失的特点,本文利用小波包优良的高频分析能力,提出一种结合SPIHT的小波包编码算法.该算法采用类似SPIHT算法的零树结构,通过重新定义方向树,即扩展方向树,改变小波包各节点之间的对应关系,解决小波包分解时产生的"父冲突"问题.同时,对扩展方向树的合理性进行了实验验证,并结合SPIHT算法实现了整个编解码.实验结果表明,对于富含纹理的遥感图像,在1bpp的压缩率下,该算法峰值信噪比(PSNR)超出SPIHT算法0.5-1dB,且视觉效果更好.     

Image coding based on wavelet feature vector

In this article, an efficient image coding scheme that takes advantages of feature vector in wavelet domain is proposed. First, a multi‐stage discrete wavelet transform is applied on the image. Then, the wavelet feature vectors are extracted from the wavelet‐decomposed subimages by collecting the corresponding wavelet coefficients. And finally, the image is coded into bit‐stream by applying vector quantization (VQ) on the extracted wavelet feature vectors. In the encoder, the wavelet feature vectors are encoded with a codebook where the dimension of codeword is less than that of wavelet feature vector. By this way, the coding system can greatly improve its efficiency. However, to fully reconstruct the image, the received indexes in the decoder are decoded with a codebook where the dimension of codeword is the same as that of wavelet feature vector. Therefore, the quality of reconstructed images can be preserved well. The proposed scheme achieves good compression efficiency by the following three methods. (1) Using the correlation among wavelet coefficients. (2) Placing different emphasis on wavelet coefficients at different decomposing levels. (3) Preserving the most important information of the image by coding the lowest‐pass subimage individually. In our experiments, simulation results show that the proposed scheme outperforms the recent VQ‐based image coding schemes and wavelet‐based image coding techniques, respectively. Moreover, the proposed scheme is also suitable for very low bit rate image coding. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 123–130, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20045     

基于QR码的DWT-SVD数字水印算法

目的 结合奇异值和小波变换的优点, 针对彩色载体图像进行水印的嵌入和提取, 提高水印的鲁棒性和安全性。方法 根据基于DWT-SVD的数字水印算法, 对彩色载体图像进行RGB到Lab颜色空间的转换, 对L分量作二级离散小波变换, 选择低频子带并对其进行奇异值分解, 同时对原始QR码水印进行奇异值分解。最后根据人眼视觉系统 （HVS） 特性, 选择合适的嵌入强度因子嵌入水印。结果 文中算法对于高斯噪声、 椒盐噪声、 斑纹噪声和泊松噪声、 JPEG压缩、 小角度的旋转攻击、 缩放攻击以及对比度增强有一定的鲁棒性。结论 该算法能够抵抗常见的水印攻击, 满足数字防伪以及版权保护的需要。     

基于小波包分解和PCA-Attention-LSTM的舰船辐射噪声识别技术

为了改进舰船辐射噪声分类系统的性能,进一步提高识别准确率,文章提出了一种基于多特征的小波包分解在长短期记忆(LongShort-TermMemory,LSTM)网络中分类的方法。该方法首先通过小波包分解技术,分频段提取舰船辐射噪声的多种特征,将提取的特征利用主成分分析法(Principal Component Analysis, PCA)进行数据降维,通过添加注意力机制(Attention Mechanism)算法的LSTM网络,对辐射噪声结果分类,提高了学习效率和识别准确率。为了更精细地提取特征,分频段提取了舰船辐射噪声的时频域特征、小波变换特征和梅尔倒谱系数等特征,并将分频段与不分频段的特征、多特征与单一特征、不同信噪比间的算法性能进行对比。实验结果表明,基于小波包分解和PCA-Attention-LSTM的模型可以有效地提高舰船辐射噪声分类的性能,是一种可行的分类方法。     

Edge preserving filter of side scan sonar images with wavelet modulus maxima shift‐correlative technique

An edge preserving filter algorithm of side scan sonar (SSS) image based on wavelet modulus maxima shift‐correlative (WMMS) technique is proposed in this article. First, the proposed WMMS algorithm decomposes SSS image into multiscale wavelet coefficients. Then the modulus maxima, which are produced by catastrophe points, are extracted from wavelet coefficients. The algorithm matches these maxima across the different scales to identify signal or noise. After correcting the “drifting” phenomenon of modulus maxima, a correlation factor array of wavelet coefficients is constructed by strengthening the maxima dominated by signal and suppressing those maxima dominated by noise. By correlating wavelet coefficients with the correlation factor array, the WMMS strengthens the useful high‐frequency signal and weakens the noise. Finally, the algorithm restores SSS image from revised wavelet coefficients. We apply the WMMS algorithm to filter SSS images of the experimental sea areas. Results show that WMMS has advantages over traditional algorithms in suppressing noise and preserving useful high‐frequency information. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 349–355, 2011     

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.     

Motion estimation and compensation based on almost shift-invariant wavelet transform for image sequence coding

There has been rapid progress in the application of wavelet transforms to image and image sequence compression. The standard discrete wavelet transform lacks transition invariance in image decomposition which will affect the accuracy of motion estimation from the decomposed subimages in video coding. In this article, we present a study of applying an almost shift-invariant wavelet transform with “oversampled frames” to image sequence compression. With minimal oversampling and biorthogonal spline wavelets in the almost shift-invariant wavelet transform, motion vectors can be more accurately estimated, contributing toward fewer prediction errors in comparison to those obtained with the standard discrete wavelet transform. Thus, an improved compression ratio can be obtained. We present two new algorithms, the full-motion oversampling algorithm (FMOS) and the reduced search multiresolution motion estimation algorithm (MRME), for estimating motion fields at different scales and in different subimages. In the latter, motion vectors at a higher resolution are approximated by the motion vector estimates at a lower resolution through proper scaling. Experiments were performed on three video sequences with a variety of motions including slow, fast, and zooming. Our results have shown that both algorithms, FMOS and MRME, using the almost shift-invariant oversampled frame wavelet transform have reduced prediction errors and enhanced the compression performance in terms of peak-signal-to-noise ratio (PSNR) for the same bit rate when compared to the existing full motion standard algorithm. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 214–229, 1998     

R-D匹配优选小波包零树图像压缩

为使R-D小波包优选算法与零树编码在量化精度上更好地匹配，对R-D小波包优选算法的寻优次序进行修改，使小波包各节点的量化精度差异不超过一位。实验结果表明，R-D匹配优选小波包零树编码可以取得与R—D优选小波包零树编码相当或比后者更好的图像压缩效果，可见R-D匹配优选算法是更适合零树编码的小波包优选算法。     

Dimensional scalable lossless compression of MRI images using haar wavelet lifting scheme with EBCOT

Modern medical imaging requires storage of large quantities of digitized clinical data. To provide high bandwidth and to reduce the storage space, a medical image must be compressed before transmission. One of the best image compression techniques is using the Haar wavelet transform. The method of discrete cosine transform (DCT) is chosen to be the preprocessing scheme to identify the image frequency information and has excellent energy compaction property. The block coding algorithm uses a wavelet transform to generate the sub band samples, which can be quantized and coded. It is more robust to errors than many other wavelet‐based schemes. In this article, simulations are carried out on different medical Images and it demonstrates the performance in terms of peak signal to noise ratio (PSNR) & bits per pixel (BPP). Our proposed method is found to preserve information fidelity while reducing the amount of data. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 175–181, 2014     

基于双伸缩因子映射的小波分形图像压缩编码方法

本文基于小波变换研究了分形图像压缩系统.根据小波系数的能量分布特性,给出了新的小波树定义与分类方法,并在小波域内建立了具有双伸缩因子的压缩映射.在将新的压缩映射应用于图像编码过程时,结合小波嵌入式零树编码思想给出了新的小波分形图像压缩方法.数值实验给出了本文方法与已有方法的比较.     

基于模糊控制的小波包多阈值语音减噪新算法

在分析小波包传统阈值去噪算法的基础上,提出了一种基于模糊控制的小波包多阈值语音减噪新算法。该算法采用改进的多阈值选取方式来代替传统的阈值选择;应用一种新阈值函数对经小波包变换后的最底层频率系数进行量化处理以确保噪声尽可能地被滤除;模糊控制器可用于对信号中的幅值跳变以及边缘粗糙等问题进行优化与修正。综合以上3种方法即可自适应地进行语音增强处理。经实验结果验证,与传统阈值算法相比,该算法能够最大程度地还原纯语音信息,有效提高了语音去噪的准确度与信噪比。     

Canny准则小波边缘检测在图像融合中的应用

提出了一种新的图像融合方法。该方法选择Canny准则作为边缘检测的基础,并结合小波变换算法,确定图像边缘位置。在小波域中,对高频信息依据其是否为边缘点采用不同的融合策略,对低频信息利用加权法进行融合,再进行小波逆变换重构融合图像。实验结果表明,提出的算法在抑制噪声的同时,能有效地突出边缘细节,更好地保持图像的空间分辨力。对于多聚焦图像的融合,偏差度为0.0520,熵为7.6609,相似度达到0.9985。     

基于阈值与人眼特性的小波图像压缩算法

针对图像传输数据量大的问题,通过结合小波变换的高效性与普遍性,提出了一种基于阈值与人眼特性的小波图像压缩算法,在图像的低频子带中通过结合阈值与人眼的视觉敏感度,提出了阈值保留算法;而图像的高频子带则是通过结合小波变换后各子带的方向性,并根据小波幅角的不同提出了边缘保留算法：在保证图像质量的同时减少了图像中的冗余信号。实验结果表明,新方法能够实现上述目标且具有低耗时的优势,在图像压缩领域具有较好的发展前景。     

基于脊波变换的扩频水印算法

针对脊波变换域比小波变换域更适合表示图像线状边缘的奇异性的特征,本文设计出一种新的图像水印算法,即将图像分块脊波变换,在脊波变化域上的高频系数中加性地嵌入经零均值互不相关伪随机序列扩频调制后的水印。实验结果表明,本文算法不仅可以抵抗如压缩、加噪、滤波和剪切等各种常见攻击,还能抵抗图像灰度增强和减弱攻击,与小波域水印算法的比较也说明了本文算法的优势。     

基于WDFB及形态膨胀的图像压缩算法

将小波和方向滤波器组结合,实现了一种非冗余的图像变换WDFB,它满足各向异性尺度关系,能更稀疏地表示诸如边缘和纹理等几何特征.利用WDFB的优势,提出一种基于形态学操作的有效的图像压缩算法.该算法利用重要树来表达子带间的相关性,同时采用形态膨胀算子来聚类子带内的重要系数.实验结果表明,新算法在PSNR指标上明显优于基于小波的压缩算法,尤其对于含有丰富纹理的图像.例如对于512×512的Barbara图,在0.25bpp压缩率下,新算法比SPIHT和MRWD算法的PSNR分别提高1.08dB和0.87dB.     

基于边缘信息的偏振图像融合算法及评价

偏振遥感图像通常都采用强度、偏振度、偏振角来表征目标偏振特性.本文提出的基于边缘信息的偏振图像融合算法是将三幅偏振图像利用离散小波变换把图像分解成不同尺度的低频和高频部分,采用小波区域窗口和子区域窗口统计把小波系数分类成边缘和非边缘系数,通过这些方法进行有效的边缘细节信息提取.在融合处理中,低频图像的小波系数平均值作为融合后的低频系数,高频细节系数根据不同区域特征选择方法以及对应输入图像小波系数的窗口区域方差来确定融合后高频小波系数.仿真实验结果表明,这样使得融合后的图像细节更真实更丰富,图像的偏振特性体现更为充分,同时减少对源图像的预处理要求,使图像在整体上有较好的视觉效果.从而证明这种方法能够在保留图像微小细节方面获得满意的结果,且算法有效性优于其他的图像融合方法.     

一种无频带错位的小波包算法

研究了数字信号处理中的小波分析方法。基于多尺度分析的思想,提出了信号的小波分解及重构新算法和小波包算法。由于算法中采用了频移技术,在小波分解中避免了频率的折叠现象,同时在小波包算法中也解决了频带的错位问题。算法在工程中更具实用价值     

一种结合小波变换和维纳滤波的图像去噪算法

目的为了有效消除噪声图像中的椒盐噪声、高斯噪声甚至混合噪声,结合维纳滤波的优势和小波分解各分量的特点,提出一种新的图像去噪算法。方法该算法先将含噪声图像进行小波变换,分离出1个低频分量和3个中高频分量,然后对低频分量进行自适应维纳滤波,对3个中高频分量用Canny算子提取边缘,最后将处理后的4个分量进行重构得到去噪后的图像。结果仿真结果表明,该算法对扫描仪引入的常见噪声均表现出较好的去噪效果,PSNR值均大于20 d B。尤其是对于高斯噪声和混合噪声,新算法去噪后的PSNR结果高于维纳滤波、软阈值小波滤波和文献[9]算法1~8 d B,效果较好。结论结合小波变换和维纳滤波的图像去噪算法,能够较好去除噪声图像的多种类型噪声,是一种较为优秀的去噪算法。