Technique for reduction of truncation artifact in chemical shift images

The authors describe a technique to reduce truncation artifacts in chemical shift images taken for in vivo magnetic resonance spectroscopy by using boundary information available from anatomic images. In this approach, high spatial frequency components are extrapolated from the observed data by making use of the boundary information. This technique has been tested with simulations and phantom experiments. Results obtained show that the technique works very well. The authors present technical details and results of computer simulations and phantom studies.     

一种鲁棒的彩色图像多功能水印算法

首先转换彩色图像的颜色空间抽取相应的灰度图像,再对灰度图像进行提升小波分解,在分解过程中利用图像压缩操作,引起近半数的小波系数增大,另一半系数减小,基于系数增减特征实现版权水印和消费者数字指纹的同时嵌入,从而获得版权认证和盗版追踪的双重功能。仿真实验表明该算法具有良好的鲁棒性和不可感知性。     

Modified algorithm for block truncation coding of monochrome images

Block truncation coding (BTC) is a recent technique used in the coding of image data. In the letter a modified technique for BTC coding of image data is presented which is algorithmically simple and hence easy to implement. This new technique uses only the first-order statistical information as `block overhead?. The new algorithm is shown to be optimum in the mean-square sense for a particular class of BTC algorithms. The letter presents the results of using the new algorithm for a typical image and compares the performance with that of the earlier algorithm for the same image.     

Regression-based prediction for blocking artifact reduction in JPEG-compressed images.

In order to reduce the blocking artifact in the Joint Photographic Experts Group (JPEG)-compressed images, a new noniterative postprocessing algorithm is proposed. The algorithm consists of a two-step operation: low-pass filtering and then predicting. Predicting the original image from the low-pass filtered image is performed by using the predictors, which are constructed based on a broken line regression model. The constructed predictor is a generalized version of the projector onto the quantization constraint set, or the narrow quantization constraint set. We employed different predictors depending on the frequency components in the discrete cosine transform (DCT) domain since each component has different statistical properties. Further, by using a simple classifier, we adaptively applied the predictors depending on the local variance of the DCT block. This adaptation enables an appropriate blurring depending on the smooth or detail region, and shows improved performance in terms of the average distortion and the perceptual view. For the major-edge DCT blocks, which usually suffer from the ringing artifact, the quality of fit to the regression model is usually not good. By making a modification of the regression model for such DCT blocks, we can also obtain a good perceptual view. The proposed algorithm does not employ any sophisticated edge-oriented classifiers and nonlinear filters. Compared to the previously proposed algorithms, the proposed algorithm provides comparable or better results with less computational complexity.     

Data truncation artifact reduction in MR imaging using a multilayer neural network

A magnetic resonance image (MRI) may contain truncation artifacts if there are not enough high-frequency data when the conventional Fourier transform method is used for reconstruction. A method for reducing the artifacts using a multilayer neural network is presented. The network consists of one linear output layer and at least one nonlinear hidden layer. The missing high-frequency components are predicted based on known low-frequency components and are used to reduce the truncation artifacts of the image. Results from a series of simulation experiments are discussed.     

A multiplicative Nakagami speckle reduction algorithm for ultrasound images

Speckle noise of ultrasound images is of multiplicative nature which degrades the image quality in terms of resolution and contrast. While there exist a number of algorithms for reduction of multiplicative Rayleigh distributed random speckle noise, the low signal-to-noise ratio (SNR) issue of the multiplicative Rayleigh noise is still not adequately resolved. In this paper, a simple 2-dimensional (2D) local intensity smoothing method is presented which transforms the Rayleigh noise contaminated in ultrasound images to Nakagami distributed noise so as to improve the SNR of processed images. A 2D total variation regularized Nakagami speckle reduction algorithm is derived based on the maximum a posteriori estimation framework, which performs well in restoring piecewise-smooth reflectivity and preserving fine details of the image. The proposed algorithm is verified by a series of computer-simulated and real ultrasound image data. It is shown that the algorithm considerably improves the quality of ultrasound images and outperforms the Rayleigh noise based speckle reduction methods in terms of speckle SNR and contrast-to-noise ratio.     

A robust blind watermarking algorithm for depth-image-based rendering 3D images

The protection of 3D contents from illegal distribution has attracted considerable attention and depth-image-based rendering (DIBR) is proved to be a promising technology for 3D image and video displaying. In this paper, we propose a new digital watermarking scheme for DIBR 3D images based on feature regions and ridgelet transform (RT). In this scheme, the center view and the depth map are made available at the content provider side. After selecting the reference points of the center view, we construct the feature regions for watermark embedding. Considering the sparse image representation and directional sensitivity of the RT, the watermark bits are embedded into the amplitudes of the ridgelet coefficients of the most energetic direction. The virtual left and virtual right views are generated from the watermarked center view and the associated depth map at the content consumer side. The watermarked view has good perceptual quality under both the objective and subjective image quality evaluations. The embedded watermark can be detected blindly with low bit error rate (BER) from the watermarked center view, the synthesized left and right views even when the views are distorted and distributed separately. The experimental results demonstrate that the proposed scheme exhibits good performance in terms of robustness against various image processing attacks. Meanwhile, our method can be robust to common DIBR processing, such as depth image variation, baseline distance adjustment and different rendering conditions. Furthermore, compared with other related and state-of-the-art methods, the proposed algorithm shows higher accuracy in watermark extraction.     

A new method of magnetic resonance image reconstruction with short acquisition time and truncation artifact reduction

A method for the reconstruction of magnetic resonance images that allows for a substantial reduction of the quantity of measured data and, therefore, of the acquisition time is described. The truncation artifact is also reduced, improving the image quality. The method is based on techniques for getting superresolution in spectral analysis such as autoregressive modeling and Prony's method. Moreover, some new ideas about the autoregressive order selection are introduced. The method is compared to the standard one for reconstructing simulated, phantom, and medical magnetic resonance images. The numerical stability and the computational cost issues of the resulting algorithm are also addressed     

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale speckle reduction methods based on generalized Gaussian and symmetric alpha-stable priors.     

Comments on "Data truncation artifact reduction in MR imaging using a multilayer neural network"

A recent paper by Yan and Mao (see ibid., vol.12, no.1, p.73-7, 1993) provided the results of using a neural network based nonlinear prediction algorithm to extrapolate truncated magnetic resonance data. The extrapolation is intended to reduce the truncation artifacts that result when reconstructing an image from a limited k-space magnetic resonance data set using the discrete Fourier transform. When attempting to quantitatively compare Yan and Mao's method with the authors' own existing constrained modeling algorithm, the authors discovered a systematic error in Yan and Mao's analysis. With the error corrected, it was found that Yan and Mao's approach worked significantly better than they have reported and was more stable in the presence of noise.     

A robust method for fingerprinting digital images

In this paper, a method to fingerprint digital images is proposed, and different watermarked copies with different identification string are made. After determining the number of the customers and the length of the watermark string, this method chooses some values inside the digital image using a characteristic function, and adds watermarks to these values in a way that can protect the product against the attacks happened by comparing two fingerprinted copies. The watermarks are a string of binary numbers -1s and 1s. Every customer will be distinguished by a series of 1s and -1s generated by a pseudo-random generator. The owner of the image can determine the number of customers and the length of the string as well as this method will add another watermarking values to watermark string to protect the product.     

A fast trellis-based rate-allocation algorithm for robust transmission of progressively coded images over noisy channels

We propose a fast trellis-based rate-allocation algorithm for robust transmission of progressively coded images over noisy channels. The algorithm, which is an improved version of a similar algorithm by Banister et al., is based on the application of the Viterbi algorithm to a search trellis. This trellis is a substantially trimmed version of the one used by Banister et al.. The proposed algorithm is applied to images encoded by the set partitioning in hierarchical trees and the Joint Photographers Expert Group 2000 for transmission over binary symmetric channels. For different total bit budgets and channel parameters, speed-up factors of up to about three orders of magnitude are achieved.     

面向块截断编码图像的安全可逆可见水印算法

互联网的广泛普及与信息处理技术的迅速发展为网络环境数字图像资源的安全通信带来了挑战。为保障网络实时环境数字图像安全流通,提出了一种基于块截断编码的数字图像可逆可见水印方案。该方案通过调整块截断编码块两个量化级以嵌入可见水印,并将加密水印信息隐藏于编码域二元位平面中,水印嵌入过程中逻辑取反运算的使用确保了原图像可恢复性。实验结果表明了该可逆可见水印方案的有效性与安全性,可应用于网络环境数字图像安全分发,为普通用户提供含可见水印的图像资源,而仅授权用户可去除可见水印浏览高质量原图像。     

Iterative deblurring for CT metal artifact reduction

Iterative deblurring methods using the expectation maximization (EM) formulation and the algebraic reconstruction technique (ART), respectively, are adapted for metal artifact reduction in medical computed tomography (CT). In experiments with synthetic noise-free and additive noisy projection data of dental phantoms, it is found that both simultaneous iterative algorithms produce superior image quality as compared to filtered backprojection after linearly fitting projection gaps. Furthermore, the EM-type algorithm converges faster than the ART-type algorithm in terms of either the I-divergence or Euclidean distance between ideal and reprojected data in the authors' simulation. Also, for a given iteration number, the EM-type deblurring method produces better image clarity but stronger noise than the ART-type reconstruction. The computational complexity of EM- and ART-based iterative deblurring is essentially the same, dominated by reprojection and backprojection. Relevant practical and theoretical issues are discussed.     

一种鲁棒性的抖动视频稳像算法

设计了一种适用于移动摄像设备获取的视频序列的基于特征匹配的鲁棒性不稳定视频稳像算法,算法首先将亮度自适应模型融入传统的KLT方法中以实现鲁棒性的特征匹配,而后基于特征误差分析和运动一致性原则对特征集合进行有效性验证以提高特征的可靠性,并提出一种基于运动往复特性的抖动检测方法以避免视频无抖动时的稳像误差,最后根据匹配的特征区域间的对应关系采用Affine运动模型进行图像稳定.实验结果表明,算法对于前景物体运动和外界光线突变具有较好的鲁棒性.     

A novel block truncation coding of color images using aquaternion-moment-preserving principle

Block truncation coding (BTC) is an efficient tool for image compression. To compress color-pixel blocks, a novel color BTC algorithm, called quaternion-moment block truncation coding (QMBTC), is presented. Analytical formulas for QMBTC, whose computation time is on the order of pixel block size, are derived by using quaternion arithmetic and the moment-preserving principle. The proposed color BTC algorithm can adaptively truncate a pixel block into one or two output classes according to the distribution of the color values inside the blocks. The experimental results show that the compression ratio is increased as compared with existing color BTC algorithms, and the picture quality of the reconstructed images is satisfactory. In addition, a post-BTC data compression scheme is proposed to further compress the subimage constructed by reproduction colors of truncated pixel blocks. Using a lookup table to display decoded data, this postprocessing scheme can output images acceptable to human eyes     

A robust eye localization algorithm for face recognition

The accuracy of face alignment affects greatly the performance of a face recognition system. Since the face alignment is usually conducted using eye positions, the algorithm for accurate eye localization is essential for the accurate face recognition. In this paper, an algorithm is proposed for eye localization. First, the proper AdaBoost detection is adaptively trained to segment the region based on the special gray distribution in the region. After that, a fast radial symmetry operator is used to precisely locate the center of eyes. Experimental results show that the method can accurately locate the eyes, and it is robust to the variations of face poses, illuminations, expressions, and accessories.     

A robust sequential detection algorithm for cardiac arrhythmiaclassification

In ibid., vol. 37, no. 9, p. 837-43 (1990) and Proc. IEEE 9th Annu. Conf. Eng. Med. Biol. Soc., p. 918-19 (1988) N.V. Thakor et al. describe a sequential probability ratio test (SPRT) based on threshold crossing intervals (TCI) for the discrimination of ventricular fibrillation (VF) from ventricular tachycardia (VT). However, in applying their algorithm to data from the MIT-BIH malignant arrhythmia database, the authors observed some overlap in the distributions of TCI for VF and VT resulting in 16% overall error rate for the discrimination. In this communication, the authors describe a modified SPRT algorithm, using a new feature dubbed blanking variability (BV) as the basis for discrimination. Using the MIT-BIH database, the preliminary results showed that the proposed method decreases the overall error rate to 5%     

多传感器图像的二次匹配算法

针对光学图像的多光谱、多传感器图像的自动配准,提出了一种基于边缘重心的多传感器图像二次匹配算法.首先,通过边缘检测和相应的后期链接处理,提取出较为完整的图像边缘;然后,根据图像边缘的链码相关以及区域不变矩策略,寻找匹配边缘,并对已匹配边缘的重心进行一致性检验获得控制点对;最后,采用最小二乘法估计图像粗匹配变换参数.在粗匹配的基础上,根据匹配后图像共有区域内边缘的主要方向和重心,再次对图像进行细匹配,修正匹配参数以提高匹配精度.多种遥感图像数据的配准实验和对比试验证实了该自动算法具有较高的可靠性和配准精度.     

一种针对JPEG图像的通用隐写分析算法

提出了一种针对JPEG图像的通用隐写分析算法。该算法提取了15个具有良好分类特性的特征参数,输入构建的KS—SVM分类器．以达到检测载密图像的目的。实验结果表明,该算法的检测正确率较高,检测速度快,能够实现针对各类JPEG载密图像的有效检测。