Multispectral filter array design without training images

Multispectral images (MSIs) have been studied for many applications; however, limitations persist in techniques to capture them due to the complexity of assembling one or more prisms and multiple sensor arrays in order to detect signals. Inspired by the application of color filter arrays to commercial digital RGB cameras, a number of researchers have studied multispectral filter arrays (MSFAs) to solve this problem. Determining the measurement wavelength and pattern of an MSFA is important for improving the quality of the demosaicked image. Some conventional studies for designing MSFAs have used training data and have optimized the measurement wavelengths and the pattern by iteratively minimizing the error between the training data and the demosaicked images. We propose a metric to evaluate an MSFA without MSIs, and optimize the measurement wavelengths and the pattern of the MSFA by minimizing the metric. The proposed metric measures the sampling distance between filters in a spatial–spectral domain and quantifies the dispersion of the sampling points by average nearest-neighbor distance (ANND) under a given arbitrary MSFA. Since the quality of the demosaicked image is assumed to be proportional to the degree of dispersion of the sampling points in the spatial–spectral domain, we optimize the MSFA by minimizing the ANND in a nested simulated annealing process. Experimental results show that the optimized MSFA obtained using our method attained a higher peak signal-to-noise ratio (PSNR) than conventional untrained MSFAs in many cases. In addition, the performance difference between some trained MSFAs and the proposed MSFA was small. We also confirmed the validity of the proposed ANND by a comparison with the mean square error obtained from MSI datasets.     

基于自适应正则化的超分辨率重建方法

传统正则化超分辨重建得到的图像往往存在过度平滑或伪信息残留的问题,结合超分辨重建模型对重建图像伪信息的产生进行了分析,针对传统方法的不足提出了基于图像区域信息自适应的正则化方法,通过图像的区域信息将图像划分为平滑区与非平滑区域,对不同区域选用不同的先验模型进行约束。同时考虑人眼的视觉感知特性,结合区域信息实现正则化参数的自适应选取。实验结果表明该方法在抑制重建图像伪信息的同时能有效保护细节,效果要优于传统方法与单一的先验模型约束,对于红外与可见光图像重建效果的提升提供了一定的理论参考。     

Joint Reconstruction of Multi-contrast Images and Multi-channel Coil Sensitivities

Magnetic resonance imaging (MRI) has an important feature that it provides multiple images with different contrasts for complementary diagnostic information. However, a large amount of data is needed for multi-contrast images depiction, and thus, the scan is time-consuming. Many methods based on parallel magnetic resonance imaging (pMRI) and compressed sensing (CS) are applied to accelerate multi-contrast MR imaging. Nevertheless, the image reconstructed by sophisticated pMRI methods contains residual aliasing artifact that degrades the quality of the image when the acceleration factor is high. Other methods based on CS always suffer the regularization parameter-selecting problem. To address these issues, a new method is presented for joint multi-contrast image reconstruction and coil sensitivity estimation. The coil sensitivities can be shared during the reconstruction due to the identity of coil sensitivity profiles of different contrast images for imaging stationary tissues. The proposed method uses the coil sensitivities as sharable information during the reconstruction to improve the reconstruction quality. As a result, the residual aliasing artifact can be effectively removed in the reconstructed multi-contrast images even if the acceleration factor is high. Besides, as there is no regularization term in the proposed method, the troublesome regularization parameter selection in the CS can also be avoided. Results from multi-contrast in vivo experiments demonstrated that multi-contrast images can be jointly reconstructed by the proposed method with effective removal of the residual aliasing artifact at a high acceleration factor.     

Application of perceptual difference model on regularization techniques of parallel MR imaging

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or to improve image SNR. The quality of reconstructed images is limited by the inaccurate estimation of the sensitivity map, noise in the acquired k-space data and the ill-conditioned nature of the coefficient matrix. Tikhonov regularization is a popular method to reduce or eliminate the ill-conditioned nature of the problem. In this approach, selection of the regularization map and the regularization parameter is very important. Perceptual difference model (PDM) is a quantitative image quality evaluation tool that has been successfully applied to varieties of MR applications. High correlation between the human rating and PDM score shows that PDM should be suitable to evaluate image quality in parallel MR imaging. By applying PDM, we compared four methods of selecting the regularization map and four methods of selecting the regularization parameter. We found that a regularization map obtained using generalized series (GS) together with a spatially adaptive regularization parameter gave the best reconstructions. PDM was also used as an objective function for optimizing two important parameters in the spatially adaptive method. We conclude that PDM enables one to do comprehensive experiments and that it is an effective tool for designing and optimizing reconstruction methods in parallel MR imaging.     

Adaptive fixed-point iterative shrinkage/thresholding algorithm for MR imaging reconstruction using compressed sensing

Recently compressed sensing (CS) has been applied to under-sampling MR image reconstruction for significantly reducing signal acquisition time. To guarantee the accuracy and efficiency of the CS-based MR image reconstruction, it necessitates determining several regularization and algorithm-introduced parameters properly in practical implementations. The regularization parameter is used to control the trade-off between the sparsity of MR image and the fidelity measures of k-space data, and thus has an important effect on the reconstructed image quality. The algorithm-introduced parameters determine the global convergence rate of the algorithm itself. These parameters make CS-based MR image reconstruction a more difficult scheme than traditional Fourier-based method while implemented on a clinical MR scanner. In this paper, we propose a new approach that reveals that the regularization parameter can be taken as a threshold in a fixed-point iterative shrinkage/thresholding algorithm (FPIST) and chosen by employing minimax threshold selection method. No extra parameter is introduced by FPIST. The simulation results on synthetic and real complex-valued MRI data show that the proposed method can adaptively choose the regularization parameter and effectively achieve high reconstruction quality. The proposed method should prove very useful for practical CS-based MRI applications.     

基于岩石重构图像的核磁共振响应模拟

能够反映孔隙介质内部真实复杂结构的CT图像经常被用于模拟研究中并能取得较好的结果. 但是,在很多情况下缺少三维CT图像或者其分辨率受限,此时,更易于获取的二维薄片图像被用于构建三维的孔隙结构图像. 假定孔隙介质各向同性,基于二维图像,使用多点统计方法可以构建岩石的三维孔隙结构. 基于数字图像,采用随机游走方法模拟得到核磁共振响应,然后由模拟得到的回波串反演得到T₂分布. 研究表明,岩石重构图像中的模拟结果与CT图像中的模拟结果有较好的一致性. 基于数字图像的核磁共振响应模拟为分析不同孔隙结构的核磁共振响应提供了便利,同时,模拟结果也为验证孔隙结构的重构效果提供了依据.     

大尺寸物光波面彩色数字全息高质量重建研究

为解决检测面尺寸较大时CCD难以得到高质量数字全息图的问题,本文利用负透镜设计光学系统让CCD接收来自物体的缩小虚像,以球面波为参考波,使用单色CCD近距离得到三种色光照射下的大尺寸彩色物体的数字全息图,然后采用可控放大率波面重建算法得到同一尺寸的数字全息重建像,合成彩色数字全息重建像.同时,使用两种消零级方法去除零级干扰,提高重建像质量,一种方法利用空间光调制器相移技术在参考光中加入一次任意相移,记录两幅数字全息图,消除重建零级像|另一种方法使用“无干扰全息图”消除重建零级像及共轭像.本文讨论结果可为大物体彩色数字全息及多波长数字全息检测应用提供有益的参考.     

Methods for quantitative image quality evaluation of MRI parallel reconstructions: detection and perceptual difference model

Many reconstruction algorithms are being proposed for parallel magnetic resonance imaging (MRI), which uses multiple coils and subsampled k-space data, and a quantitative method for comparison of algorithms is sorely needed. On such images, we compared three methods for quantitative image quality evaluation: human detection, computer detection model and a computer perceptual difference model (PDM). One-quarter sampling and three different reconstruction methods were investigated: a regularization method developed by Ying et al., a simplified regularization method and an iterative method proposed by Pruessmann et al. Images obtained from a full complement of k-space data were also included as reference images. Detection studies were performed using a simulated dark tumor added on MR images of fresh bovine liver. Human detection depended strongly on reconstruction methods used, with the two regularization methods achieving better performance than the iterative method. Images were also evaluated using detection by a channelized Hotelling observer model and by PDM scores. Both predicted the same trends as observed from human detection. We are encouraged that PDM gives trends similar to that for human detection studies. Its ease of use and applicability to a variety of MRI situations make it attractive for evaluating image quality in a variety of MR studies.     

Optimization of hyperparameters for SMS reconstruction

PurposeSimultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices with a single radiofrequency pulse. Overlapping slices encoded in acquired signal are separated using a mathematical model, which requires estimation of image reconstruction kernels using calibration data. Several parameters used in SMS reconstruction impact the quality and fidelity of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure that accelerated acquisition does not significantly degrade resulting image quality.MethodsGradient-echo echo planar imaging data were acquired with a range of SMS acceleration factors from a cohort of five volunteers with no known neurological pathology. Images were collected using two available phased-array head coils (a 48-channel array and a reduced diameter 32-channel array) that support SMS. Data from these coils were identically reconstructed offline using a range of coil compression factors and reconstruction kernel parameters. A hybrid space (k-x), externally-calibrated coil-by-coil slice unaliasing approach was used for image reconstruction. The image quality of the resulting reconstructed SMS images was assessed by evaluating correlations with identical echo-planar reference data acquired without SMS. A finger tapping functional MRI (fMRI) experiment was also performed and group analysis results were compared between data sets reconstructed with different coil compression levels.ResultsBetween the two RF coils tested in this study, the 32-channel coil with smaller dimensions clearly outperformed the larger 48-channel coil in our experiments. Generally, a large calibration region (144–192 samples) and small kernel sizes (2–4 samples) in k_y direction improved image quality. Use of regularization in the kernel fitting procedure had a notable impact on the fidelity of reconstructed images and a regularization value 0.0001 provided good image quality. With optimal selection of other hyperparameters in the hybrid space SMS unaliasing algorithm, coil compression caused small reduction in correlation between single-band and SMS unaliased images. Similarly, group analysis of fMRI results did not show a significant influence of coil compression on resulting image quality.ConclusionsThis study demonstrated that the hyperparameters used in SMS reconstruction need to be fine-tuned once the experimental factors such as the RF receive coil and SMS factor have been determined. A cursory evaluation of SMS reconstruction hyperparameter values is therefore recommended before conducting a full-scale quantitative study using SMS technologies.     

Fast multi-contrast MRI reconstruction

Multi-contrast magnetic resonance imaging (MRI) is a useful technique to aid clinical diagnosis. This paper proposes an efficient algorithm to jointly reconstruct multiple T1/T2-weighted images of the same anatomical cross section from partially sampled k-space data. The joint reconstruction problem is formulated as minimizing a linear combination of three terms, corresponding to a least squares data fitting, joint total variation (TV) and group wavelet-sparsity regularization. It is rooted in two observations: 1) the variance of image gradients should be similar for the same spatial position across multiple contrasts; 2) the wavelet coefficients of all images from the same anatomical cross section should have similar sparse modes. To efficiently solve this problem, we decompose it into joint TV regularization and group sparsity subproblems, respectively. Finally, the reconstructed image is obtained from the weighted average of solutions from the two subproblems, in an iterative framework. Experiments demonstrate the efficiency and effectiveness of the proposed method compared to existing multi-contrast MRI methods.     

Phase-shifting digital holography with burst imaging method

The application of a burst imaging method to digital holography (DH) with multiple image capturing is firstly demonstrated. Nobody has discussed this approach explicitly, although the burst imaging method is not so uncommon. The burst imaging method intermittently captures several images with a high repetition rate followed by a relatively long rest time. If the light wave from an object has a much smaller complex amplitude change in the frame interval of an image sensor than the precision of the DH system, the wave can be regarded as being in a static state. As a result, this solves a fatal problem in DH for an object that moves or deforms while capturing multiple interference images. In this research, the burst imaging method was applied to phase-shifting DH, and it is simply implemented by a high-speed image sensor and a continuous phase shifting which is good for high-speed phase shifting. The measurement error was analytically solved for the movement speed of an object. Finally, the proposed method was experimentally used to observe an evaporating droplet of ethanol on a glass substrate.

     

A novel three-dimensional imaging method by means of coded cameras array recording and computational reconstruction

In this paper, we propose a novel three-dimensional imaging method by which the object is captured by a coded cameras array (CCA) and computationally reconstructed as a series of longitudinal layered surface images of the object. The distribution of cameras in array, named code pattern, is crucial for reconstructed images fidelity when the correlation decoding is used. We use DIRECT global optimization algorithm to design the code patterns that possess proper imaging property. We have conducted primary experiments to verify and test the performance of the proposed method with a simple discontinuous object and a small-scale CCA including nine cameras. After certain procedures such as capturing, photograph integrating, computational reconstructing and filtering, etc., we obtain reconstructed longitudinal layered surface images of the object with higher signal-to-noise ratio. The results of experiments show that the proposed method is feasible. It is a promising method to be used in fields such as remote sensing, machine vision, etc.     

The formation of large scale reconstructed images through the use of holograms

We propose and demonstrate a method which constructs large size multicolor images using a holographic technique in limited spaces, i.e., tunnels. A traffic sign image of the same size and color as an actual traffic sign board is displayed using many comparative small holograms. Each hologram is composed of a single computer generated hologram (CGH). Though it was necessary to make many CGHs, we have shortened the hologram production time by putting a reconstructed image on long distance, and using many reconstruction sources. Moreover, though cross-talk images are caused in a color reconstruction, by adjusting the optical system it is possible to make sure that drivers see only the appropriate image. Many holograms function as one big hologram, and it has been confirmed that a large reconstructed image can be displayed in a limited space.     

A new strategy for respiration compensation, applied toward 3D free-breathing cardiac MRI

In thorax and abdomen imaging, image quality may be affected by breathing motion. Cardiac MR images are typically obtained while the patient holds his or her breath, to avoid respiration-related artifacts. Although useful, breath-holding imposes constraints on scan duration, which in turn limits the achievable resolution and SNR. Longer scan times would be required to improve image quality, and effective strategies are needed to compensate for respiratory motion. A novel approach at respiratory compensation, targeted toward 3D free-breathing cardiac MRI, is presented here. The method aims at suppressing the negative effects of respiratory-induced cardiac motion while capturing the heart's beating motion. The method is designed so that the acquired data can be reconstructed in two different ways: First, a time series of images is reconstructed to quantify and correct for respiratory motion. Then, the corrected data are reconstructed a final time into a cardiac-phase series of images to capture the heart's beating motion. The method was implemented, and initial results are presented. A cardiac-phase series of 3D images, covering the entire heart, was obtained for two free-breathing volunteers. The present method may prove especially useful in situations where breath-holding is not an option, for example, for very sick, mentally impaired or infant patients.     

Modified computational integral imaging-based double image encryption using fractional Fourier transform

In this paper, we propose an image encryption technique to simultaneously encrypt double or multiple images into one encrypted image using computational integral imaging (CII) and fractional Fourier transform (FrFT). In the encryption, each of the input plane images are located at different positions along a pickup plane, and simultaneously recorded in the form of an elemental image array (EIA) through a lenslet array. The recorded EIA to be encrypted is multiplied by FrFT with two different fractional orders. In order to mitigate the drawbacks of occlusion noise in computational integral imaging reconstruction (CIIR), the plane images can be reconstructed using a modified CIIR technique. To further improve the solution of the reconstructed plane images, a block matching algorithm is also introduced. Numerical simulation results verify the feasibility and effectiveness of the proposed method.     

基于有限脉冲响应滤波器的数字全息零级像消除

针对数字全息中零级像的存在影响数字再现像的质量,分析数字全息图的记录、再现原理及频谱特性,提出了一种利用有限脉冲响应滤波器消除全息图数字再现中零级像的方法.该方法只需记录一幅数字全息图,不需要相移器材或其他辅助设备,直接利用数字图像处理对数字全息图在空域进行预处理,消除全息再现时的零级像干扰.对比了数字模拟和实验拍摄到的全息图在应用有限脉冲响应滤波器消除零级像前后的再现结果,表明该方法可消除零级衍射像,改善重建像质量,并且算法简单.     

Three-dimensional imaging and visualization of partially occluded objects using axially distributed stereo image sensing

In this Letter, we propose a multiperspective three-dimensional (3D) imaging system using axially distributed stereo image sensing. In this proposed method, the stereo camera is translated along its optical axis and multiple axial elemental image pairs for a 3D scene are collected. The captured elemental images are reconstructed in 3D using a computational reconstruction algorithm based on ray back-projection. The proposed method is applied to partially occluded object visualization. Optical experiments are performed to verify the approach.     

基于空域非均匀傅里叶变换的傅里叶望远镜

为了在稀疏发射阵列下清晰重构目标图像,提出了一种基于空域非均匀傅里叶变换(NDFT)的傅里叶望远镜信号处理方法。依据傅里叶望远镜的发射器位置与抽取的目标空间频率关系,结合MATLAB程序特点,完成了空域非均匀傅里叶逆变换,重构了目标图像。稀疏发射阵列配置方式为:T型阵列单臂放置11个发射望远镜,连续抽取目标的8个低频信息,再抽取3个高频分量。选择不同形状和灰度分布的4个卫星作为成像目标。与补零均匀快速傅里叶变换(FFT)方法重构的图像对比发现:信噪比为100 dB时,相比补零均匀FFT方法, NDFT方法重构图像的Strehl比都有所提升,最高提升了0.159 8。     

Depth-control method for integral imaging

We present a method for controlling the depth of three-dimensional (3D) images reconstructed by integral photography. Incoherent light is reflected from 3D objects, propagates through a lens array, and is captured as the first elemental images by a capturing device. The second elemental images of the 3D images are generated by numerical processing from the first elemental images in accordance with the desired depth. The optical reconstruction of 3D images at the desired depth by the second elemental images is confirmed experimentally.