Robust, object-based high-resolution image reconstruction fromlow-resolution video

We propose a robust, object-based approach to high-resolution image reconstruction from video using the projections onto convex sets (POCS) framework. The proposed method employs a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the quality of the reconstructed image. Procedures for the computation of the validity map and segmentation map are presented. Experimental results demonstrate the improvement in image quality that can be achieved by the proposed methods.     

Confocal microwave imaging for breast cancer detection: delay-multiply-and-sum image reconstruction algorithm

A new image reconstruction algorithm, termed as delay-multiply-and-sum (DMAS), for breast cancer detection using an ultra-wideband confocal microwave imaging technique is proposed. In DMAS algorithm, the backscattered signals received from numerical breast phantoms simulated using the finite-difference time-domain method are time shifted, multiplied in pair, and the products are summed to form a synthetic focal point. The effectiveness of the DMAS algorithm is shown by applying it to backscattered signals received from a variety of numerical breast phantoms. The reconstructed images illustrate improvement in identification of embedded malignant tumors over the delay-and-sum algorithm. Successful detection and localization of tumors as small as 2 mm in diameter are also demonstrated.     

Dielectric profiles reconstruction via the quadratic approach in 2-D geometry from multifrequency and multifrequency/multiview data

Deals with the reconstruction of the contrast function of a dielectric cylinder with rectangular cross section starting from the knowledge of the electric scattered far field produced under the incidence of plane waves. We analyze the set of the reconstructable Fourier harmonics of the unknown permittivity contrast function with linear and quadratic approaches. This set depends on the ranges of the wavenumbers /spl beta/, of the angles of incidence /spl theta//sub i/ of the impinging plane waves, and of the observation angles /spl theta//sub o/. We discuss a simple way to describe such a dependence, which allows us to find out that the set of the retrievable harmonics for the quadratic approach contains that for the linear one. Moreover, our investigation points out how increasing the amount of independent data through a multifrequency/multiview measurement scheme allows us to enlarge the set of the retrievable unknown harmonics with respect to a multifrequency/single-view one. Our analysis is confirmed by numerical results. Memory storage requirements and processing time consumption for the quadratic approach are greatly reduced thanks to the massive use of the fast Fourier transform algorithm.     

Multiobjective neural network for image reconstruction

The authors propose a multiobjective neural network model and algorithm for image reconstruction from projections. This model combines the Hopfield model and multiobjective decision making approach. A weighted sum optimisation based neural network algorithm is developed. The dynamic process of the net is based on minimisation of a weighted sum energy function and Euler's iteration and this algorithm is applied to image reconstruction from computer-generated noisy projections and Siemens Somaton DR scanner data, respectively. Reconstructions based on this method are shown to be superior to those based on conventional iterative reconstruction algorithms such as MART (multiplicate algebraic reconstruction technique) and convolution from the point of view of accuracy of reconstruction. Computer simulation using the multiobjective method shows a significant improvement in image quality and convergence behaviour over conventional algorithms     

参考图像集的鲁棒性层次结构模型

针对文物色彩复原中彩色参考图像集建立的多因素定性决策问题,提出一种基于层次分析法的色彩复原层次结构模型。通过史料记载、专家经验并结合现场调研中对实际褪色问题的分析,归纳出能代表文物色彩的本体元素,研究其与褪色的内在联系。利用专家群组决策,由色彩元素的采集、知识发现、主要影响因素确定、层次结构分析、建立专家判断矩阵、判断矩阵的求解、建立带权重的色彩复原层次结构模型共七步,建立色彩复原层次结构模型。采用两级滤波法逐层求解模型判断矩阵权重,实现基于该模型的参考图像集查找系统。实验结果表明,该方法能够较准确地为褪色图像查找参考图像组合,给后续色彩复原提供科学依据。     

Robust template matching for affine resistant image watermarks

Digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyrighted material. This paper describes a method for the secure and robust copyright protection of digital images. We present an approach for embedding a digital watermark into an image using the Fourier transform. To this watermark is added a template in the Fourier transform domain to render the method robust against general linear transformations. We detail a new algorithm based on polar maps for the accurate and efficient recovery of the template in an image which has undergone a general affine transformation. We also present results which demonstrate the robustness of the method against some common image processing operations such as compression, rotation, scaling, and aspect ratio changes.     

Robust statistical fusion of image labels

Image labeling and parcellation (i.e., assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifacts. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both reduce this variability and quantify the degree of uncertainty. Existing techniques have exploited maximum a posteriori statistics to combine data from multiple raters and simultaneously estimate rater reliabilities. Although quite successful, wide-scale application has been hampered by unstable estimation with practical datasets, for example, with label sets with small or thin objects to be labeled or with partial or limited datasets. As well, these approaches have required each rater to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters in a research or clinical environment. Herein, we propose a robust approach to improve estimation performance with small anatomical structures, allow for missing data, account for repeated label sets, and utilize training/catch trial data. With this approach, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables many individuals to collaborate in the construction of large datasets for labeling tasks (e.g., human parallel processing) and reduces the otherwise detrimental impact of rater unavailability.     

Improved image reconstruction techniques for synthetic apertureradiometers

Ripple in excess of that attributable to Gibb's phenomenon has been identified in brightness temperature images created from data collected by synthetic aperture radiometers used for Earth remote sensing. The cause of the excess ripple is identified, and methods for reducing its effect on image quality are proposed     

Fast spatio-temporal image reconstruction for dynamic PET

In tomographic imaging, dynamic images are typically obtained by reconstructing the frames of a time sequence independently, one by one. A disadvantage of this frame-by-frame reconstruction approach is that it fails to account. For temporal correlations in the signal. Ideally, one should treat the entire image sequence as a single spatio-temporal signal. However, the resulting reconstruction task becomes computationally intensive. Fortunately, as the authors show in this paper, the spatio-temporal reconstruction problem call be greatly simplified by first applying a temporal Karhunen-Loeve (KL) transformation to the imaging equation. The authors show that if the regularization operator is chosen to be separable into space and time components, penalized weighted least squares reconstruction of the entire image sequence is approximately equivalent to frame-by-frame reconstruction in the space-KL domain. By this approach, spatio-temporal reconstruction can be achieved at reasonable computational cost. One can achieve further computational savings by discarding high-order KL components to avoid reconstructing them. Performance of the method is demonstrated through statistical evaluations of the bias-variance tradeoff obtained by computer simulation reconstruction     

Block-iterative methods for image reconstruction from projections

The simultaneous MART algorithm (SMART) and the expectation maximization method for likelihood maximization (EMML) are extended to block-iterative versions, BI-SMART and BI-EMML, that converge to a solution in the feasible case, for any choice of subsets. The BI-EMML reduces to the "ordered subset" EMML of Hudson et al. (1992, 1994) when their "subset balanced" property holds.     

Biorthogonal wavelet system for high-resolution image reconstruction

High-resolution images are often desired but made impossible because of hardware limitations. For the high-resolution model proposed by Bose and Boo (see Int. J. Imaging Syst. Technol., vol.9, p.294-304, 1998), the iterative wavelet-based algorithm has been shown to perform better than the traditional least square method when the resolution ratio M is two and four. In this paper, we discuss the minimally supported biorthogonal wavelet system that comes from the mathematical model by Bose and Boo and propose a wavelet-based algorithm for arbitrary resolution ratio M/spl ges/2. The numerical results indicate that the algorithm based on our biorthogonal wavelet system performs better in high-resolution image reconstruction than the wavelet-based algorithm in the literature, as well as the common-used least square method.     

Grouped-coordinate ascent algorithms for penalized-likelihoodtransmission image reconstruction

Presents a new class of algorithms for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. We derive the algorithms by applying to the transmission log-likelihood a version of the convexity technique developed by De Pierro for emission tomography. The new class includes the single-coordinate ascent (SCA) algorithm and Lange's convex algorithm for transmission tomography as special cases. The new grouped-coordinate ascent (GCA) algorithms in the class overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission maximum likelihood-expectation maximization (ML-EM) algorithm or in the SCA algorithm. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradient-based methods. (3) The algorithms are easily parallelizable, unlike the SCA algorithm and perhaps line-search algorithms. We show that the GCA algorithms converge faster than the SCA algorithm, even on conventional workstations. An example from a low-count positron emission tomography (PET) transmission scan illustrates the method     

Subband-coded image reconstruction for lossy packet networks

Transmission of digital subband-coded images over lossy packet networks presents a reconstruction problem at the decoder. This paper presents two techniques for reconstruction of lost subband coefficients, one for low-frequency coefficients and one for high-frequency coefficients. The low-frequency reconstruction algorithm is based on inherent properties of the hierarchical subband decomposition. To maintain smoothness and exploit the high intraband correlation, a cubic interpolative surface is fit to known coefficients to interpolate lost coefficients. Accurate edge placement, crucial for visual quality, is achieved by adapting the interpolation grid in both the horizontal and vertical directions as determined by the edges present. An edge model is used to characterize the adaptation, and a quantitative analysis of this model demonstrates that edges can be identified by simply examining the high-frequency bands, without requiring any additional processing of the low-frequency band. High-frequency reconstruction is performed using linear interpolation, which provides good visual performance as well as maintains properties required for edge placement in the low-frequency reconstruction algorithm. The complete algorithm performs well on loss of single coefficients, vectors, and small blocks, and is therefore applicable to a variety of source coding techniques.     

Two-dimensional hybrid element image reconstruction for TMillumination

A Newton's iterative scheme for electromagnetic imaging is proposed for reconstruction of the dielectric properties of inhomogeneous, lossy bodies with arbitrary shape. The algorithm is based on a finite element (FE) representation which is coupled to a boundary element (BE) formulation for the forward solution of the electric fields; together these are termed the hybrid element method. It utilizes FE discretization of only the area of interest while incorporating the RE method to match the conditions of the homogeneous background region extending to infinity. This paper presents image reconstruction for the 2D TM polarization case where two classes of dielectric distributions are studied which demonstrate the flexibility of this method along with some of the difficulties associated with larger imaging problems     

Robust classwise and projective low-rank representation for image classification

Several variations of the low-rank representation have been suggested intensively for diverse applications, recently. They perform properly on image alignment but undesirably on classification. That is, they are intractable when a new image arrives with an unknown label to be classified. Hence, inspired by a recent research of the fast projection, this paper proposes a supervised approach called the robust classwise and projective low-rank representation (CPLRR), which is the first attempt to align images classwise and learn a projective nonlinear function, simultaneously. It separates out the low-rank components explicitly with the parametric transformation corrections and projects the original images to the low-rank representations of corresponding categories, in an efficient manner. With the advantage of fast projection, CPLRR is appropriate for image classification. Extensive experiments conducted on MNIST, Extended Yale B, and CMU PIE datasets validate the effect of the robust low-rank alignment and the rapid projection, against different domain deformations, noises, and illumination conditions.     

Multiple-incidence and multifrequency for profile reconstruction of random rough surfaces using the 3-D electromagnetic fast multipole model

A fast algorithm for reconstructing the profile of random rough surfaces using electromagnetic scattering data is presented. The algorithm is based on merging a fast forward solver and an efficient optimization technique. The steepest descent fast multipole method is used as the three-dimensional fast forward solver. A rapidly convergent descent method employing a "marching-on" strategy for processing multifrequency and multi-incidence angle data is introduced to minimize an underlying cost function. The cost function represents the error between true (synthetic) and simulated scattered field data. Several key issues that impact the accuracy in reconstructing the rough profile are examined in this work, e.g., the location and number of receivers, the incident and scattered directions, the surface roughness, and details regarding the manner in which sensitivity information is computed in the inversion scheme. The results show that using the multiple-incidence (one angle at a time) and the multifrequency (one frequency at a time) strategies lead to improve the profile reconstruction.     

扫描隧道显微镜图像重建的研究

在扫描隧道显微镜轮廓测量过程中,探针自身的几何形状会混入测量结果中,从而造成相应的测量误差。本文提出了根据对标准样品的测量结果,反算出探针的几何形状的算法,并应用于实没图像示解出探针的几何形状。在求得探针的几何形状后,文中推导了去除探针几何形状造成的误差,对扫描隧道显微镜图像进行重建的算法,并对实测样品进行了处理。结果表明上术坷有效地减少小探针自形状造成的测量误差。     

Three-dimensional EIT imaging of breast tissues: system design and clinical testing

Results of development and testing of the new medical imaging system are described. The system uses a planar array consisting of 256 electrodes and enables obtaining images of the three-dimensional conductivity distribution in regions below the skin's surface up to several centimeters deep. The developed measuring system and image reconstruction algorithm can be used for breast tissue imaging and diagnostics, in particular for malignant tumor detection. Examples of tomographic images obtained in vivo during clinical tests are presented. The mammary gland, being an organ-target, alters at the background with such physiological events as menstrual cycle, pregnancy, lactation, and postmenopause. The objectives of this paper include estimation of the possibilities of electrical impedance mammography for investigation of mammary glands' state among women with different hormonal status. We found that electrical impedance mammograms from different groups had clear visual distinctions and statistically significant differences in mammary glands' conductivity. Our data on conductivity distribution in the mammary gland during different physiological periods will allow us to use it as normal values in the future, to continue this research on mammary glands with different pathology.     

Morphologic gain-controlled regularization for edge-preserving super-resolution image reconstruction

Total Variation or Bilateral Total variation-based regularization of ill-posed super-resolution (SR) problem is well established. However, the SR image reconstructed by this method produces ringing artifacts near strong edges. Second, the extension of SR Imaging to SR video always desire faster SR reconstruction process. We develop a gain-controlled-based locally adaptive regularization technique for SR reconstruction for faster convergence and more detail reconstruction while suppressing the ringing artifacts. We present an iterative process for the model and perform a series of numerical experiments to show evidence of the good performance of the numerical scheme and the proposed gain-controlled regularization.     

Regularized image reconstruction algorithms for positron emission tomography

We develop algorithms for obtaining regularized estimates of emission means in positron emission tomography. The first algorithm iteratively minimizes a penalized maximum-likelihood (PML) objective function. It is based on standard de-coupled surrogate functions for the ML objective function and de-coupled surrogate functions for a certain class of penalty functions. As desired, the PML algorithm guarantees nonnegative estimates and monotonically decreases the PML objective function with increasing iterations. The second algorithm is based on an iteration dependent, de-coupled penalty function that introduces smoothing while preserving edges. For the purpose of making comparisons, the MLEM algorithm and a penalized weighted least-squares algorithm were implemented. In experiments using synthetic data and real phantom data, it was found that, for a fixed level of background noise, the contrast in the images produced by the proposed algorithms was the most accurate.