Image fusion using non-separable wavelet frame

In this paper, an image fusion method is proposed based on the non-separable wavelet frame (NWF) for merging a high-resolution panchromatic image and a low-resolution multispectral image. The low-frequency part of the panchromatic image is directly substituted by multispectral image. As a result, the multispectral information of the multispectral image can be preserved effectively in the fused image. Due to multiscale method for enhancing the high-frequency parts of the panchromatic image, spatial information of the fused image can be improved. Experimental results indicate that the proposed method outperforms the intensity-hue-saturation (IHS) transform, discrete wavelet transform and separable wavelet frame in preserving spectral and spatial information.     

Moment searching algorithm for bioluminescence tomography

To avoid the ill-posedness in the inverse problem of bioluminescence tomography, a moment searching algorithm fusing the finite element method （FEM） with the moment concept in theoretical mechanics is developed. In the algorithm, the source＇s information is mapped to the surface photon flux density by FEM, and the source＇s position is modified with the feedback through the algorithm of barycenter searching, which makes full use of the position information of the photon flux density on surface. The position is modified in every iterative step and will finally converge to the real source＇s value theoretically.     

A Pan-sharpening method appropriate to vegetation applications

An improved Pan-sharpening algorithm appropriate to vegetation applications is proposed to fuse a set of IKONOS panchromatic （PAN） and multispectral image （MSI） data. The normalized difference vegetation index （NDVI） is introduced to evaluate the quality of fusion products. Compared with other methods such as principal component analysis （PCA）, wavelet transform （WT）, and curvelet transform （CT）, this algorithm has a better trade-off between keeping the spatial and spectral information. The NDVI performances indicate that the fusion product of this method is more suitable for vegetation applications than the other methods.     

A Compton scattering image reconstruction algorithm based on total variation minimization

Compton scattering imaging is a novel radiation imaging method using scattered photons.Its main characteristics are detectors that do not have to be on the opposite side of the source,so avoiding the rotation process.The reconstruction problem of Compton scattering imaging is the inverse problem to solve electron densities from nonlinear equations,which is ill-posed.This means the solution exhibits instability and sensitivity to noise or erroneous measurements.Using the theory for reconstruction of sparse images,a reconstruction algorithm based on total variation minimization is proposed.The reconstruction problem is described as an optimization problem with nonlinear data-consistency constraint.The simulated results show that the proposed algorithm could reduce reconstruction error and improve image quality,especially when there are not enough measurements.     

Pseudo-global tomography for local micro-computed tomography with high-brightness synchrotron X-rays

The computed tomography imaging of a local region inside a sample with a size larger than the field of view is particularly important for synchrotron X-ray imaging.In this letter,an improved algorithm is proposed to reconstruct the local structure inside a sample using almost completely local data.The algorithm significantly reduces the X-ray radiation dose and improves computational efficiency.Simulation results show that the new algorithm works well and has a higher reconstruction precision than previous methods,as confirmed by experimental results carried out at the Shanghai Synchrotron Radiation Facility.     

Optical reconstruction of 3D images by use of pure-phase computer-generated holograms

A three-dimensional （3D） object reconstruction technique that uses pure-phase computer-generated holograms （CGHs） and a phase-only spatial light modulator （SLM） is proposed. The full parallax CGHs are generated by the point source method and the wave-oriented method without paraxial approximation. Different from conventional CGHs, the pure-phase information on the hologram plane is loaded on the SLM to reconstruct the 3D diffusive objects without considering the reference wave. This technique is more efficient in its utilization of the space-bandwidth product of the SLMs. Numerical simulations and experiments are performed, and the results show that our proposed method can reconstruct 3D diffusive objects successfully.     

3-D flame temperature field reconstruction with multiobjective neural network

A novel 3-D temperature field reconstruction method is proposed in this paper, which is based on multi-wavelength thermometry and Hopfield neural network computed tomography. A mathematical model of multi-wavelength thermometry is founded, and a neural network algorithm based on multiobjective optimization is developed. Through computer simulation and comparison with the algebraic reconstruction technique (ART) and the filter back-projection algorithm (FBP), the reconstruction result of the new method is discussed in detail. The study shows that the new method always gives the best reconstruction results. At last, temperature distribution of a section of four peaks candle flame is reconstructed with this novel method.     

Numerical studies of penetration problems by an improved particle method

A particle mapping transportation algorithm was proposed on the basis of the particle-in-cell method.The particles with rectangular influence domains were employed in the transportation algorithm to reduce the numerical fluctuations.Based on the error analysis in the process of particle motion computation,a prediction-correction algorithm was introduced to improve the computational accuracy.Furthermore,the performance of the particle mapping transportation method was evaluated by using the rotation,the slotted disk and the shear advection tests,and the results were compared with other interface reconstruction methods.Finally,the hemispherical projectile penetration into a steel target was numerically simulated.The results showed that the proposed method produced less numerical fluctuations and exhibited clear material interfaces,which indicated that it is accurate and effective.     

Region stepwise reconstruction method based on two source–detector-separation groups for reconstructing background optical properties of two-layered slab sample

The accuracy of the background optical properties has a considerable effect on the quality of reconstructed images in near-infrared functional brain imaging based on continuous wave diffuse optical tomography(CW-DOT). We propose a region stepwise reconstruction method in CW-DOT scheme for reconstructing the background absorption and reduced scattering coefficients of the two-layered slab sample with the known geometric information. According to the relation between the thickness of the top layer and source– detector separation, the conventional measurement data are divided into two groups and are employed to reconstruct the top and bottom background optical properties, respectively. The numerical simulation results demonstrate that the proposed method can reconstruct the background optical properties of two-layered slab sample effectively. The region-of-interest reconstruction results are better than those of the conventional simultaneous reconstruction method.     

Preliminary attempt on maximum likelihood tomosynthesis reconstruction of DEI data

Tomosynthesis is a three-dimension reconstruction method that can remove the effect of superim- position with limited angle projections. It is especially promising in mammography where radiation dose is concerned. In this paper, we propose a maximum likelihood tomosynthesis reconstruction algorithm （ML-TS） on the apparent absorption data of diffraction enhanced imaging （DEI）. The motivation of this contribution is to develop a tomosynthesis algorithm in low-dose or noisy circumstances and make DEI get closer to clinic application. The theoretical statistical models of DEI data in physics are analyzed and the proposed algorithm is validated with the experimental data at the Beijing Synchrotron Radiation Facility （BSRF）. The results of ML-TS have better contrast compared with the well known ＇shift-and-add＇ algorithm and FBP algorithm.     

Source sparsity based primal-dual interior-point method for three-dimensional bioluminescence tomography

In this paper, an efficient l₁-regularized reconstruction method named the primal-dual interior-point (PDIP) method is presented for three-dimensional bioluminescence tomography (BLT) based on the adaptive finite element framework. Taking into account the sparse characteristic of the bioluminescent source, the BLT inverse problem is considered to be a linear programming problem and is represented by its primal and dual form. The source localization and quantification are obtained by solving the primal-dual Newton equation system. The comparisons between PDIP and the classical conjugate gradient least square (CGLS) algorithm are implemented to validate our method. Results from numerical simulation and an in vivo mouse experiment demonstrate the credibility and the potential of the proposed method in practical BLT reconstruction.     

An efficient method for model refinement in diffuse optical tomography

Diffuse optical tomography (DOT) is a non-linear, ill-posed, boundary value and optimization problem which necessitates regularization. Also, Bayesian methods are suitable owing to measurements data are sparse and correlated. In such problems which are solved with iterative methods, for stabilization and better convergence, the solution space must be small. These constraints subject to extensive and overdetermined system of equations which model retrieving criteria specially total least squares (TLS) must to refine model error. Using TLS is limited to linear systems which is not achievable when applying traditional Bayesian methods. This paper presents an efficient method for model refinement using regularized total least squares (RTLS) for treating on linearized DOT problem, having maximum a posteriori (MAP) estimator and Tikhonov regulator. This is done with combination Bayesian and regularization tools as preconditioner matrices, applying them to equations and then using RTLS to the resulting linear equations. The preconditioning matrixes are guided by patient specific information as well as a priori knowledge gained from the training set. Simulation results illustrate that proposed method improves the image reconstruction performance and localize the abnormally well.     

Recent advances in bioluminescence tomography: methodology and system as well as application

Optical molecular imaging has been rapidly developed to noninvasively visualize in vivo physiological and pathological processes involved in normal and suffering organisms at the cellular and molecular levels, in which advanced optical imaging technology and modern molecular biology are being combined to provide a state‐of‐the‐art tool for preclinical biomedical research. Among optical molecular imaging modalities, bioluminescence tomography (BLT) has experienced considerable growth and attracted much attention in recent years for its excellent performance, unique advantages, and high cost‐effectiveness. This article focuses on the genesis and development of BLT, especially for its computational methodology, imaging system, and biomedical application. An overview of the advantages and challenges of the conventional planar bioluminescence imaging technique is first described in comparison with currently available molecular imaging modalities. The imaging algorithms for inverse source reconstruction are classified and summarized according to different a priori knowledge, followed by a simple depiction of the uniqueness theorems of BLT solution. Diverse imaging systems for obtaining three‐dimensional quantitative information of internal bioluminescent sources are then reviewed. The latest application examples of BLT in tumor study and drug discovery are introduced and compared with other mature imaging technologies. Finally, the paper is concluded and an attractive prospect for BLT is predicted.     

Tomographic reconstruction using prior with adaptively binary neighborhood

Bayesian approaches can be used to improve ill-posed image reconstruction by regularizing the inverse solution using spacial or temporal neighborhood information. This paper proposes a prior with adaptively binary neighborhood (ABN prior) for statistical tomographic reconstruction. With binary weight map adapted to local image features, the proposed prior can lead to improved reconstruction by including relevant pixels belonging to similar structures and excluding those not. A two-step algorithm is also put forward for tomographic reconstruction using the proposed prior. Experiments using both simulated and clinical computerized tomography (CT) data are performed to validate the reconstructions with the proposed ABN prior.     

Early-photon guided reconstruction method for time-domain fluorescence lifetime tomography

A reconstruction method guided by early-photon fluorescence yield tomography is proposed for time-domain fluorescence lifetime tomography(FLT) in this study. The method employs the early-arriving photons to reconstruct a fluorescence yield map, which is utilized as a priori information to reconstruct the FLT via all the photons along the temporal-point spread functions. Phantom experiments demonstrate that, compared with the method using all the photons for reconstruction of fluorescence yield and lifetime maps, the proposed method can achieve higher spatial resolution and reduced crosstalk between different targets without sacrificing the quantification accuracy of lifetime and contrast between heterogeneous targets.     

最佳摄动量法在光学计算机层析图像重建逆问题中的应用

阐述了时间分辨光学计算机层析（ＣＴ）图像重建塑问题的最佳摄动量法求解原理,人出了基于微扰变分原理的正向算子雅可比（Ｊａｃｏｂｉ）矩阵计算公式,数值模拟实验证明了该方法在实际应用中的可行性。     

融合结构先验信息的稳态扩散光学断层成像重建算法研究

扩散光学断层成像作为一种无辐射损伤、低成本的光学在体成像技术,有着良好的应用前景,但具有空间分辨率低、难以定量的缺陷.为了提高扩散光学断层成像的分辨率,实现光学参数分布的精确重建,基于有限元方法,提出了融合结构先验信息的稳态扩散光学断层成像重建算法.该算法以扩散近似作为成像模型,通过软先验的Laplace 正则化方法引入由MicroCT提供的空间结构信息.采用伴随法计算Jacobian矩阵,Levenberg-Marquardt方法用来进行迭代优化.仿真结果表明该算法不仅能获得精确的光学参数值分布,而且显著地提高了迭代收敛的速度.     

An image reconstruction algorithm based on Bayesian theorem for electrical resistance tomography

Electrical resistance tomography (ERT) is considered to be one of the most promising process tomography techniques for two-phase/multiphase flow measurement due to the advantages such as high speed, low cost and non-intrusive sensing. The iterative image reconstruction algorithm based on Bayesian theorem has been derived to solve the ERT inverse problem. It is taken into account, which is different to the existing ERT image reconstruction algorithms, including the prior probability of the permittivity distribution and the noise information in the measurement data. Both simulation and experimental data were carried out for typical two-phase flow regimes. The flow regimes are identified according to the reconstructed images of the Bayesian iteration method and conventional methods. Results obtained indicate that the Bayesian iteration method improves the reconstructed image quality with the traditional linear back projection (LBP) and reweighted least squares (RLS). Therefore, the Bayesian iteration method is suitable for identification of online two-phase flow regimes.     

A multi-phase level set framework for source reconstruction in bioluminescence tomography

We propose a novel multi-phase level set algorithm for solving the inverse problem of bioluminescence tomography. The distribution of unknown interior source is considered as piecewise constant and represented by using multiple level set functions. The localization of interior bioluminescence source is implemented by tracing the evolution of level set function. An alternate search scheme is incorporated to ensure the global optimal of reconstruction. Both numerical and physical experiments are performed to evaluate the developed level set reconstruction method. Reconstruction results show that the proposed method can stably resolve the interior source of bioluminescence tomography.     

基于多尺度均值漂移的高分辨率遥感影像快速分割方法

均值漂移算法是一种特征空间分析方法,广泛应用于自然场景影像和医学影像分割中.但算法较高的计算复杂度成为其在具有海量特性的遥感影像中应用的瓶颈.文章将均值漂移算法拓展到小波域,提出了一种小波域均值漂移快速分割算法.多光谱遥感影像和仿真影像的实验表明:在获得相当的分割结果的前提下,相比单尺度均值漂移算法,提出的分割算法能够...