Embedded image compression based on wavelet pixel classification and sorting

The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering is limited to "pixel sorting," the coding order of wavelet pixels. We use pixel classification and sorting to provide a better understanding of previous works. The image pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel pixel classification and sorting (PCAS) scheme in wavelet domain. In PCAS, pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of pixel classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed pixel classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity.     

A CMOS Image Sensor for Multi-Level Focal Plane Image Decomposition

An alternative image decomposition method that exploits prediction via nearby pixels has been integrated on the CMOS image sensor focal plane. The proposed focal plane decomposition is compared to the 2-D discrete wavelet transform (DWT) decomposition commonly used in state of the art compression schemes such as SPIHT and JPEG2000. The method achieves comparable compression performance with much lower computational complexity and allows image compression to be implemented directly on the sensor focal plane in a completely pixel parallel structure. A CMOS prototype chip has been fabricated and tested. The test results validate the pixel design and demonstrate that lossy prediction based focal plane image compression can be realized inside the sensor pixel array to achieve a high frame rate with much lower data readout volume. The features of the proposed decomposition scheme also benefit real-time, low rate and low power applications.      

Adaptive homogeneity-directed demosaicing algorithm.

A cost-effective digital camera uses a single-image sensor, applying alternating patterns of red, green, and blue color filters to each pixel location. A way to reconstruct a full three-color representation of color images by estimating the missing pixel components in each color plane is called a demosaicing algorithm. This paper presents three inherent problems often associated with demosaicing algorithms that incorporate two-dimensional (2-D) directional interpolation: misguidance color artifacts, interpolation color artifacts, and aliasing. The level of misguidance color artifacts present in two images can be compared using metric neighborhood modeling. The proposed demosaicing algorithm estimates missing pixels by interpolating in the direction with fewer color artifacts. The aliasing problem is addressed by applying filterbank techniques to 2-D directional interpolation. The interpolation artifacts are reduced using a nonlinear iterative procedure. Experimental results using digital images confirm the effectiveness of this approach.     

基于自适应预处理的图像分割方法

为了防止分水岭算法过分割问题,该文提出了一种基于自适应预处理的图像分割算法。该方法在分水岭算法的基础上,首先结合像素点亮度特征和空间分布特性应用自适应方法对梯度图像进行预处理。通过考察各像素点邻域中像素分类后的分布情况,来判断考察点是处于区域中心还是处于边界,并据此对考察点的梯度值进行调节。然后在预处理后的梯度图像上选定标记,将预处理后的梯度图像中大于200个像素的连通区域标定为标记。最后用分水岭分割方法对带标记的参考图像进行分割。试验结果表明,该分割方法具有良好的分割效果。     

基于二维最大熵阈值分割的SIFT图像匹配算法

提出了一种基于二维灰度直方图最大熵阈值分割的SIFT图像特征匹配算法。与传统SIFT算法相比,该算法首先综合利用图像像素的灰度信息和邻域空间信息,生成图像二维灰度直方图,并基于此直方图的最大熵对图像进行阈值分割,然后检测分割后图像的DoG尺度空间局部极值,并以此作为特征点进行图像匹配。实验结果表明,基于所提出的匹配算法,可以有效降低图像背景噪声和边缘像素点对目标匹配的干扰,进而提高图像目标的匹配性能。     

基于色差空间的Bayer 图像的迭代插值算法

单CCD/CMOS 传感器相机捕捉图像信息靠在传感器表面覆盖一层颜色滤波阵列(CFA),经过CFA 后每个像素点只能获得物理三基色(红,绿,蓝)其中一种分量。另外缺少的两种颜色分量,需要通过周围像素的值来估算。首先利用55 模板内的像素来估计插值的方向并用最优的权重系数来插值G 分量。其次利用了基于有理函数的二维插值算子在色差空间插值R(B)处缺少的B(R)分量。再次利用色差插值G 处缺少的R 和B 分量。最后,使用方差约束条件,迭代插值过程被重复多次直到达到了最优的插值结果。通过在24 幅柯达图片以及笔者相机拍摄的图片上的Matlab 仿真实验,结果显示,被提出的算法无论是在视觉方面还是在量化的数据方面都表现出了优势。     

Incremental algorithm-a new fast backprojection scheme for parallel beam geometries

A fast backprojection scheme for parallel beam geometries is proposed. Known as the incremental algorithm, it performs backprojection on a ray-by-ray (beam-by-beam) basis rather than the pixel-by-pixel backprojection in the conventional algorithm. By restructuring a conventional backprojection algorithm, the interdependency of pixel computations (position and value) is transformed to a set of incremental relations for a beam, where a beam is a set of pixels enclosed by two adjacent rays in 2-D computed tomography (CT), and a set of voxels enclosed by four adjacent rays in 3-D CT. To minimize the overhead of searching for the next pixels, a searching flow technique has been developed to implement the first-order and second-order incremental relations for 2-D and 3-D CTs, respectively. The values of all the pixels in each beam (except the first pixel) are computed with additions only, the key idea of the proposed backprojection scheme. The incremental algorithm has been implemented on two different machines and compared to B.F. Shepp and L.A. Logan's (1974) algorithm. The present implementation results show the superiority of this approach over the conventional algorithm.     

A Bayes decision test for detecting uncovered-background and movingpixels in image sequences

We present a ternary hypothesis test for the detection of stationary, moving, and uncovered-background pixels between two image frames in a noisy image sequence using the Bayes decision criterion. Unlike many uncovered-background detection schemes, our scheme does not require motion estimation for the differentiation between moving pixels and uncovered-background pixels. We formulate the Bayes decision rule using a single intensity-difference measurement at each pixel and using multiple intensity-difference measurements in the neighborhood of each pixel. We quantitatively evaluate our detection algorithm on an image sequence which we have generated and qualitatively on the Trevor White image sequence.     

A Hybrid Compression Method for Integral Images Using Discrete Wavelet Transform and Discrete Cosine Transform

Integral imaging (II) is a promising three-dimensional (3-D) imaging technique that uses an array of diffractive or refractive optical elements to record the 3-D information on a conventional digital sensor. With II, the object information is recorded in the form of an array of subimages, each representing a slightly different perspective of the object In order to obtain high-quality 3-D images, digital sensors with a large number of pixels are required. Consequently, high-quality II involves recording and processing large amounts of data. In this paper, we present a compression method developed for the particular characteristics of the digitally recorded integral image. The compression algorithm is based on a hybrid technique implementing a four-dimensional transform combining the discrete wavelet transform and the discrete cosine transform. The proposed algorithm outperforms the baseline JPEG compression scheme applied to II and a previous compression method developed for II based on MPEG II.     

Deterministic prediction in progressive coding

Deterministic prediction in progressive coding of images is investigated. Progressive coding first creates a sequence of resolution layers by beginning with an original image and reducing its resolution several times by factors of some natural number M. The resultant layers are losslessly encoded, beginning with the lowest-resolution layer and, then encoding each higher resolution image incrementally upon the previous one. Coding efficiency may be improved if knowledge of the rules which produced the lower-resolution image of each pair is used to deterministically predict pixels of the higher, so they need not be encoded. Given reduction rules expressing each low-resolution pixel as a function of nearby high-resolution pixels and previously generated low-resolution pixels, it is shown that finding a complete set of rules, each of which deterministically predicts the value of a high-resolution pixel when certain values are found in nearby low-resolution pixels and previously coded high-resolution pixels, is NP-complete. A recursive algorithm for solving the problem in optimal time as a depth-first tree search is proposed, and the characteristics of the resultant prediction process are studied     

Subpixel Target Location Techniques for 3—D Coordinate Measuring System

The close photogrammetric 3-D coordinate meaurement is a new measuring technology in the fields of the coordinate measurement machine (CMM) in recent yearsl.In this method,we usually place some targets on the measured object and take image of targets to determine the object coordinate.The subpixel location of target image plays an important role in high accuracy 3-D coordinate measuring procedure,In this paper,some subpixel location methods are reviewed and some factors which affect location precision are analyzed. Then we propose bilinear interpolation centroid algorithm.The experiments have shown this algorithm can improve accuracy of target centroid by increasing available pixels.     

Reversible data-hiding for progressive image transmission

This research presents a multi-resolution reversible data-hiding algorithm to enable multi-scale marked images that are transmitted progressively to be exactly recovered at the receiver side once hidden data has been extracted. Based on the spatially hierarchical multi-layer structures of progressive-image transmission, the proposed algorithm first decimates the incoming image pixels into a pre-specified number of hierarchical layers of pixels. Then, it modifies pixel values in each hierarchical layer by shifting the interpolated-difference-values histogram between two neighboring layers of pixels to embed secret information into the corresponding hierarchical layer images. The proposed algorithm offers a reversible data-hiding ability for applications that use progressive image transmission to render progressive-image authentication, information-tagging, covert communications, etc. With progressive-reversible data-hiding, users of progressive image transmission can receive each original progressive image and complete hidden messages related to the received progressive image. This allows users to make real-time definite decisions according to an application's requirements. In contrast to other reversible data-hiding schemes, the algorithm proposed in this study features reversible data-hiding in progressive-image transmission based on a hierarchical decimation and interpolation technique. The interpolating process is used to reduce the difference values between the target pixel values in one progressive layer and their interpolated ones. This increases the hiding capacity of interpolation-differences histogram shifting. The experimental results demonstrate that the proposed method provides a greater embedding capacity and maintains marked images at a higher quality. Moreover, the proposed method has a low computational complexity as it requires only simple arithmetic computations.     

区域指导的激光水下图像插值算法

提出了一种区域指导的激光水下图像插值算法(简称AIU).AIU算法首先采用小波变换,对低频成分进行特殊处理,以抑制散斑噪声和增强关注的目标;然后将图像分割成目标区域和背景区域,结合近邻法和众数法确定待插值点所属区域,对区域内部点采用线性插值;对于区域间的过渡点,设计非线性插值公式,对与待插值点属于同一区域的邻域像素,分配较大的权值,对相邻区域的邻域像素分配较小的权值.实验结果表明,AIU算法可以克服传统插值算法的边缘模糊问题,使目标更完整清晰.     

一种改进的完全搜索块匹配算法

完全搜索块匹配算法是图像序列稳定中常用的运动估计方法,在传统的完全搜索块匹配算法中,在搜索区域中图像序列的当前帧和参考帧对应的像素对对匹配结果的贡献是完全一样的,这样使得算法的性能容易受到待稳定的图像序列中各种噪声的影响。针对这个不足,提出了一种改进的完全搜索块匹配算法。在新的匹配准则中,搜索区域中图像序列的当前帧和参考帧对应的像素对对匹配结果是否有贡献取决于它们灰度值之差的绝对值与预先设定的门限值进行大小比较的结果,对应的灰度值之差的绝对值不超过该门限值的像素对对匹配结果有贡献,而且贡献大小受到与像素对位置相关的加权处理,否则该像素对对匹配结果无贡献。实验结果证明了该算法的优越性。     

改进的邻域滤波图像修复方法

在分析了几种重要的基于滤波图像修复算法基础上,提出了一种改进的邻域滤波图像修复算法.为了减少平滑处理中的模糊,该算法根据待修复点与窗口中已知点的欧氏距离来决定相应位置的权值,然后采用邻域中已知像素的加权平均来估算待修复点的值.大量实验结果表明,改进算法修复的视觉效果和归一化均方误差(NMSE)的计算结果均明显优于先前的...     

View generation for three-dimensional scenes from video sequences

This paper focuses on the representation and view generation of three-dimensional (3-D) scenes. In contrast to existing methods that construct a full 3-D model or those that exploit geometric invariants, our representation consists of dense depth maps at several preselected viewpoints from an image sequence. Furthermore, instead of using multiple calibrated stationary cameras or range scanners, we derive our depth maps from image sequences captured by an uncalibrated camera with only approximately known motion. We propose an adaptive matching algorithm that assigns various confidence levels to different regions in the depth maps. Nonuniform bicubic spline interpolation is then used to fill in low confidence regions in the depth maps. Once the depth maps are computed at preselected viewpoints, the intensity and depth at these locations are used to reconstruct arbitrary views of the 3-D scene. Specifically, the depth maps are regarded as vertices of a deformable 2-D mesh, which are transformed in 3-D, projected to 2-D, and rendered to generate the desired view. Experimental results are presented to verify our approach.     

非局部相似性去马赛克算法

单传感器数码相机得到的色彩图像在每一个像素点处只有一种色彩值,为了得到一幅全彩色图像,需要在每一个像素位置上估计出另外两个缺失的色彩值。现有主要算法都是利用像素的相关性进行估计和插值,在那些边缘色彩跳变处和色彩高饱和度处容易估计失误,出现所谓的马赛克失真。为了克服这类马赛克现象,本文提出了一种利用图像的非局部相似性,即利用处于图像中不同位置处的像素点往往表现出很强的相关性这一特点,结合图像内容的局部平坦度自适应去马赛克的插值算法。该算法,首先根据相似度函数搜索与被插像素最相似的像素,然后利用区域水平和垂直方向的梯度组算子来计算区域的平坦度,从而根据相似程度和平坦度自适应地选择图像块进行插值。实验结果表明,相对于传统插值算法,该算法提高了图像的峰值信噪比,锐化了图像的纹理和边缘,减少了虚假色和锯齿现象,改善了图像的视觉效果。      

基于单向差值扩展的彩色图像可逆数据隐藏

针对Tian差值扩展算法存在过分修改像素值、须嵌入定位图等缺点,提出一种基于单向差值扩展的彩色图像可逆数据隐藏算法。利用色彩分量间的相关性减小差值,并用较小的差值扩展量扩展单个色彩分量的像素值,扩展方向由上溢和下溢像素的数量决定;采用预先调整像素值的方法避免像素溢出,少量的调整信息即可代替定位图,嵌入容量大幅提高;提取端根据临界像素值的顺序定位调整像素的位置,用调整信息恢复调整像素,因此不需要嵌入定位图,在提取信息的同时可无损地恢复原始图像。实验结果表明,本文算法在保证图像质量的同时,嵌入容量有很大的提高。     

基于改进的K-L变换的多光谱图像压缩算法

融合离散小波变换(DWT,discrete wavelet tran sform)与Karhunen-Loeve变换(KLT),将图像的能量集中到少数系数上,以达到更好的 压缩效果。首先将多光谱图像的每个谱段进行快速9/72D DWT,消除多光谱图像的大部分 空间冗余;然后对所有谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空 间冗余;最后对所得谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空间冗 余;最后对所得系数进行熵编码,得到压缩码流。实验结果表明,在码率为0.25～2.0bit/ pixel范围内,平均信噪比(SNR)高于41dB,同时缩短了运 算时间,从而提升了多光谱图像压 缩算法的性能。     

一种基于逐点阈值分割的图像边缘检测方法

提出一种对图像中每个点逐一取阈值进行分类的一种新的阈值分割算法.该算法利用图像中像素邻域的灰度值均值统计信息作为该点阈值设置的标准,并引入该点邻域内像素灰度值方差作为附加判断条件,使提取出来的日标点是图像的边缘点.事实上该阈值分割算法起到了边缘提取的效果.实验证明,本文算法起到了良好的边缘检测效果,并且验证了本算法对于以邻域统计信息作为阈值估计标准的合理性.