基于小波变换的医学图象压缩编码

本文研究了医学图象小波变换系数的统计特征。根据医学图象的特点，提出了基于小波变换的医学图象编码方法。实验表明，这种方法具有较好的压缩编码性能，优于JPEG标准。同时，它支持逐次浮现式传输，从而能满足医学图象存储、通信、检索的需要。     

多层离散小波变换系数的稀疏向量构造的改进

融合离散小波变换和压缩感知的图像压缩方案很好避免了采用离散余弦变换和压缩感知时所带来的块效应,但当前基于单层离散小波变换的算法压缩比较低,基于多层离散小波变换的算法重构质量不佳。为了解决这些不足,根据离散小波变换系数的特点,对现有基于多层离散小波变换的算法提出了改进。图像经小波变换后,保留图像最高层低频系数,高频系数的构造方式给予适当改进。实验结果表明,与现有算法相比,重构图像的PSNR值得到2～4 dB提高。     

Sharing secret images using integer single or multiple wavelet transform

Integer wavelet transform or multiple wavelet transform has been found attractive in image and video applications where its scaling function(s) and wavelet(s) provide higher energy compaction in smooth (scaling) subband and smaller values of detail (wavelet) coefficients. This paper presents a study on an image sharing method applying the integer single or multiple wavelet transform and Shamir’s (r, m) threshold scheme that provide highly compact and easily manageable shadows for real time progressive transmission. Experimental results are given to illustrate the characteristics of this method.     

Progressive image transmission by transform coefficient residualerror quantization

A progressive image transmission scheme based on iterative transform coding structure is proposed for application in interactive image communication over low-bandwidth channels. The scheme not only provides progressive transmission, but also guarantees lossless reproduction combined with a degree of compression. The image to be transmitted undergoes an orthogonal transform, and the transform coefficients are quantized (scalar or vector) before transmission. The novelty is that the residual error array due to quantization is iteratively fedback and requantized (scalar or vector); the coded residual error information is progressively transmitted and utilized in reconstructing the successive approximations. It is shown that the average reconstruction error variance converges to zero as the number of iterative stages approaches infinity. In practice, lossless reproduction can be achieved with a small number of iterations by using an entropy coder on the final residual-error image. Computer simulation results demonstrate the effectiveness of the technique     

改进的基于àtrous小波的遥感图像融合方法

针对多光谱与全色图像的融合,提出了一种改进的基于àtrous小波变换的图像融合方法。首先对多光谱图像进行IHS(intensity-hue-saturation)变换,然后对PAN图像进行àtrous小波变换,再将PAN图像的细节分量添加到多光谱图像的强度分量中。为了控制添加的数量,从小波系数中提取出一个重要性测度函数,并根据阈值选择重要的PAN特征,用新的强度分量来取代多光谱图像的强度分量。最后再做IHS逆变换得到融合图像。实验结果表明,和IHS变换算法及常用的小波融合算法相比,本文提出的融合方法在光谱信息的保持与空间细节信息的增强两个方面的综合性能得到提高,具有更好的效果。     

Clustering compressed sensing based on image block similarities

Compressed sensing （CS） algorithm enables sampling rates significantly under classical Nyquist rate without sacrificing reconstructed image quality. It is known that, a great number of images have many similar areas which are composed by the same number of grayscale or color. A new CS scheme, namely clustering compressed sensing （CCS）, was proposed for image compression, and it introduces clustering algorithm onto framework of CS based on similarity of image blocks. Instead of processing the image as a whole, the image is firstly divided into small blocks, and then the clustering algorithm was proposed to cluster the similar image blocks. Afterwards, the optimal public image block in each category is selected as the representative for transmission. The discrete wavelet transform （DWT） and Gaussian random matrix are applied to each optimal public image block to obtain the random measurements. Different from equal measurements, the proposed scheme adaptively selects the number of measurements based on different sparsity of image blocks. In order to further improve the performance of the CCS algorithm, the unequal-CCS algorithm based on the characteristics of wavelet coefficients was proposed as well. The low frequency coefficients are retained to ensure the quality of reconstructed image, and the high frequency coefficients are compressed by the CCS algorithm. Experiments on images demonstrate good performances of the proposed approach.     

Image coding using wavelet transform and classified vectorquantisation

The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and classified vector quantisation is presented. The input image is first decomposed into a hierarchy of three layers containing ten subimages by the discrete wavelet transform. The lowest resolution low frequency subimage is scalar quantised with 8 bits/pixel. The high frequency subimages are compressed by classified vector quantisation to utilise the crosscorrelation among different resolutions while reducing the edge distortion and computational complexity. Vectors are constructed by combining the corresponding wavelet coefficients of different resolutions in the same orientation and classified according to the magnitude and the position of wavelet transform coefficients. Simulation results show that the proposed scheme has a better performance than those utilising current scalar or vector quantisation schemes     

基于尺度乘积和相关性的图像去噪

为了去除图像的噪声,提出了一种基于尺度乘积和尺度相关性的平稳小波交换图像去噪方法.在传统小波系数估计的基础上,考虑到尺度间的相关性,利用不同尺度小波系数形成的系数向量,通过线性最小均方误差估计小波系数,获得各个高频子带的估计系数.针对单纯利用尺度间相关性去噪造成的图像边缘失真问题,在不同尺度小波系数形成的系数向量中引入了小波系数乘积,不但可以较好区分边缘信息和噪声信息,而且提高了原有算法的去噪能力.仿真结果表明,该图像去噪算法能有效去除图像噪声,较好保持图像边缘,在峰值信噪比和视觉质量上都有较大提高.     

一种基于提升方案小波和小波块的图像水印算法

数字水印技术是多媒体信息安全领域的一个重要研究课题。文章提出一种基于提升方案小波和小波块的图象水印算法。首先，利用提升方案小波对宿主图像和水印图像进行分解，同时利用小波系数的树结构，将宿主图像的小波系数块按纹理密度分成两类，并根据嵌入位置所隶属的小波块的纹理强弱，嵌入不同强度的水印信息，这在一定程度上保证了所嵌入水印在不可见性前提下的极大鲁棒性。实验结果验证了所提出算法的有效性。     

A progressive transmission image coder using linear phase uniformfilterbanks as block transforms

This paper presents a novel image coding scheme using M-channel linear phase perfect reconstruction filterbanks (LPPRFBs) in the embedded zerotree wavelet (EZW) framework introduced by Shapiro (1993). The innovation here is to replace the EZWs dyadic wavelet transform by M-channel uniform-band maximally decimated LPPRFBs, which offer finer frequency spectrum partitioning and higher energy compaction. The transform stage can now be implemented as a block transform which supports parallel processing and facilitates region-of-interest coding/decoding. For hardware implementation, the transform boasts efficient lattice structures, which employ a minimal number of delay elements and are robust under the quantization of lattice coefficients. The resulting compression algorithm also retains all the attractive properties of the EZW coder and its variations such as progressive image transmission, embedded quantization, exact bit rate control, and idempotency. Despite its simplicity, our new coder outperforms some of the best image coders published previously in the literature, for almost all test images (especially natural, hard-to-code ones) at almost all bit rates.