Edge-preserving image compression using adaptive lifting wavelet transform

In this paper, a novel 2-D adaptive lifting wavelet transform is presented. The proposed algorithm is designed to further reduce the high-frequency energy of wavelet transform, improve the image compression efficiency and preserve the edge or texture of original images more effectively. In this paper, a new optional direction set, covering the surrounding integer pixels and sub-pixels, is designed. Hence, our algorithm adapts far better to the image orientation features in local image blocks. To obtain the computationally efficient and coding performance, the complete processes of 2-D adaptive lifting wavelet transform is introduced and implemented. Compared with the traditional lifting-based wavelet transform, the adaptive directional lifting and the direction-adaptive discrete wavelet transform, the new structure reduces the high-frequency wavelet coefficients more effectively, and the texture structures of the reconstructed images are more refined and clear than that of the other methods. The peak signal-to-noise ratio and the subjective quality of the reconstructed images are significantly improved.     

Signal and image approximation using interval wavelet transform.

In signal approximation, classical wavelet synthesis are known to produce Gibbs-like phenomenon around discontinuities when wavelet coefficients in the cone of influence of the discontinuities are quantized. By analyzing a function in a piecewise manner, filtering across discontinuities can be avoided. Using this principle, the interval wavelet transform can generate sparser representations in the vicinity of discontinuities than classical wavelet transforms. This work introduces two new constructions of interval wavelets and shows how they can be used for image compression and upscaling.     

基于小波零交叉基元的多分辨率图像匹配方法

文中将小波变换运用到立体视觉的图像匹配中,选取合适的二进小波基,以二进小波变换的多尺度零交叉为匹配基元,较多地利用了图像的结构特征。在匹配过程中采用由粗到精的层决匹配策略和严格的匹配约束,减小了对应点搜索范围,并从局部和整体两个角度进行匹配后的质量控制。实验结果证明,该方法具有较高的匹配精确度和较快的匹配速度,并且算法稳定。     

Efficient blind image restoration using discrete periodic Radon transform

Restoring an image from its convolution with an unknown blur function is a well-known ill-posed problem in image processing. Many approaches have been proposed to solve the problem and they have shown to have good performance in identifying the blur function and restoring the original image. However, in actual implementation, various problems incurred due to the large data size and long computational time of these approaches are undesirable even with the current computing machines. In this paper, an efficient algorithm is proposed for blind image restoration based on the discrete periodic Radon transform (DPRT). With DPRT, the original two-dimensional blind image restoration problem is converted into one-dimensional ones, which greatly reduces the memory size and computational time required. Experimental results show that the resulting approach is faster in almost an order of magnitude as compared with the traditional approach, while the quality of the restored image is similar.     

A modified statistical approach for image fusion using wavelet transform

The fusion of images is an important technique within many disparate fields such as remote sensing, robotics and medical applications. For image fusion, selecting the required region from input images is a vital task. Recently, wavelet-based fusion techniques have been effectively used to integrate the perceptually important information generated by different imaging systems about the same scene. In this paper, a modified wavelet-based region level fusion algorithm for multi-spectral and multi-focus images is discussed. Here, the low frequency sub-bands are combined, not averaged, based on the edge information present in the high frequency sub-bands, so that the blur in fused image can be eliminated. The absolute mean and standard deviation of each image patch over 3 × 3 window in the high-frequency sub-bands are computed as activity measurement and are used to integrate the approximation band. The performance of the proposed algorithm is evaluated using the entropy, fusion symmetry and peak signal-to-noise ratio and is compared with recently published results. The experimental result proves that the proposed algorithm performs better in many applications.     

小波变换帧间预测医学图像编码

该文报道了一种新的医学图像编码方法,称为具有运动补偿的小波变换帧间预测医学图像编码。模拟结果表明:这种新算法比现有的同类算法有更好的性能。     

Multi-spectral and hyperspectral image fusion using 3-D wavelet transform

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.     

一种提升格式的非线性插值小波图像编码方法

在Sweldens等人提出的提升格式(Lifting Scheme)的基础上,提出了一种非线性的避免边缘的小波变换方法.针对信号的局部特性选择提升格式中的预测环节,同时将如何选择预测环节的信息保存在小波系数中,避免了额外的存储空间.对于保存选择信息的小波系数做简单的可逆变换,使其不会因为量化和压缩编码等原因而丢失,从而保证逆变换的稳定性.     

基于小波变换的因特网图像传输

基于因特网的图像传输,使用的协议通常是无损协议TCP/IP。无损协议对待丢失的数据包要求重传,这大大增加了传输时间。因此,提出了一种基于小波变换的因特网图像传输方案,能大大缩短通过因特网的图像传输时间。     

基于小波变换的多规则图像融合方法

针对红外和可见光图像各自特征,提出了基于小波变换多分辨率的不同规则图像融合方法.首先对原图像进行多级小波分解,产生多频带小波系数,利用像素的局部区域方差、平均梯度及掩膜滤波等方法对不同频带小波系数计算融合权重,最后将小波融合系数逆变换实现图像的融合处理.实验结果表明,融合图像特征显著,易于观察.     

基于边缘的小波图像融合算法

提出一种基于边缘的小波图像融合算法,针对小波分解的不同频率域,分别对高频系数和低频系数设计不同的融合规则。采用小波系数局部模极大和定义的局部信号强度比相结合的方法融合高频系数;对低频系数采用一种基于边缘的方法,在两幅原图像中选择最有可能是边缘的点加以保留。实验结果表明:采用此融合规则得到的融合图像具有良好的融合效果。     

Curved wavelet transform for image coding.

The conventional two-dimensional wavelet transform used in existing image coders is usually performed through one-dimensional (1-D) filtering in the vertical and horizontal directions, which cannot efficiently represent edges and lines in images. The curved wavelet transform presented in this paper is carried out by applying 1-D filters along curves, rather than being restricted to vertical and horizontal straight lines. The curves are determined based on image content and are usually parallel to edges and lines in the image to be coded. The pixels along these curves can be well represented by a small number of wavelet coefficients. The curved wavelet transform is used to construct a new image coder. The code-stream syntax of the new coder is the same as that of JPEG2000, except that a new marker segment is added to the tile headers. Results of image coding and subjective quality assessment show that the new image coder performs better than, or as well as, JPEG2000. It is particularly efficient for images that contain sharp edges and can provide a PSNR gain of up to 1.67 dB for natural images compared with JPEG2000.     

基于小波变换的图像重构算法研究

小波变换是当前图像处理、应用数学和工程学科中一个迅速发展的领域,它具有多分辨率分析的特性,同时又在变换域有表征信号局部特征的能力,能有效地从信号中提取信息。本课题以MATLAB作为平台,研究小波变换的mallet算法,对一维离散采样信号进行滤波和重采样,并扩展到多维信号中。根据算法结果,对图像进行小波分解,重构其近似信...     

小波变换在图像压缩过程中的作用

介绍图像压缩原理,分析小波变换用于图像压缩的一般步骤,基于多分辨分析的理论基础,重点与详细的说明了小波变换在图像压缩中的数学计算过程所起到的作用,并用MATLAB给予模拟演示.     

Predictive subband image coding with wavelet transform

Wavelet transform can decompose images into various multiresolution subbands. In these subbands the correlation exists. A novel technique for image coding by taking advantage of the correlation is addressed. It is based on predictive edge detection from the LL band of the lowest resolution level to predict the edge in the LH, HL and HH bands in the higher resolution level. If the coefficient is predicted as an edge it is preserved; otherwise, it is discarded. In the decoder, the location of the preserved coefficients can also be found as in the encoder. Therefore, no overhead is needed. Instead of complex vector quantization, which is commonly used in subband image coding for high compression ratio, simple scalar quantization is used to code the remaining coefficients and achieves very good results.     

离散小波变换在医学图像中的应用

本文以三维医学体数据作为研究对象,充分利用离散小波变换,对其进行多分辨率分解,把三维医学体数据分解成三个不同方向上的子医学体数据。实验结果表明,三维医学体数据经过离散小波变换后被分割成八个子频带,其主要能量和特征集中在低频子带系数中,可以用来提取特征向量构造零水印。     

基于小波变换的中波红外偏振图像融合

针对中波红外偏振图像的特征,给出了一种基于小波变换的融合新方法。根据中波红外偏振图像高低频图像特点选择不同的融合规则,针对低频图像采用偏振融合的方法来抑制背景信息,针对高频图像采用超分辨率重建算法来提升细节信息。实验结果显示,证融合后的红外图像比原始图像更加清晰,相较于仅偏振处理图像获得了更高的分辨率,相较于传统小波融合算法增加了偏振方面细节。     

Synthetic aperture radar image compression using tree-structured edge-directed orthogonal wavelet packet transform

Currently, wavelet-based coding algorithms are popular for synthetic aperture radar (SAR) image compression, which is very important for reducing the cost of data storage and transmission in relatively slow channels. However, standard wavelet transform is limited by spatial isotropy of its basis functions that is not completely adapted to represent image entities like edges or textures, which means wavelet-based coding algorithms are suboptimal to image compression. In this paper, a novel tree-structured edge-directed orthogonal wavelet packet transform is proposed for SAR image compression. Inspired by the intrinsic geometric structure of images, the new transform improves the performance of standard wavelet by filtering along the regular direction first and then along the orthogonal direction with directional lifting structure. The cost function of best basis selection is designed by textural and directional information for tree-structured edge-directed orthogonal wavelet packet transform. The new transform including speckle reduction can be used to construct SAR image coder with the embedded block coding with optimal truncation for transform coefficients, and arithmetic coding for additional information. The experimental results show that the proposed approach outperforms JPEG2000 and Fast wavelet packet (FWP), both visually and item of PSNR values.     

Image coding using wavelet transform

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.     

基于KL与小波联合变换的多光谱图像压缩

鉴于卫星拍摄的遥感图像的空间分辨率和光谱分辨率越来越高,在一些应用中,常会对多光谱图像进行压缩。为了提高多光谱图像的压缩质量,提出了联合相位相关和仿射变换的图像配准方法,有效提高了图像谱段之间的相关性。针对多光谱图像压缩,提出了结合Karhunen-Love,KL变换去除谱间相关和嵌入式二维小波编码方法。相比JPEG2000谱段图像独立压缩方法,提出方法解压图像的Peak Signal to Noise Ratio,PSNR值平均提高2.1 dB。实验结果表明:所提出的方法能在相同的压缩率下获得比JPEG2000谱段图像独立压缩方法更好的图像质量。