Experimental validation of a linear model for data reduction in chirp-pulse microwave CT

Chirp-pulse microwave computerized tomography (CP-MCT) is an imaging modality developed at the Department of Biocybernetics, University of Niigata (Niigata, Japan), which intends to reduce the microwave-tomography problem to an X-ray-like situation. We have recently shown that data acquisition in CP-MCT can be described in terms of a linear model derived from scattering theory. In this paper, we validate this model by showing that the theoretically computed response function is in good agreement with the one obtained from a regularized multiple deconvolution of three data sets measured with the prototype of CP-MCT. Furthermore, the reliability of the model as far as image restoration in concerned, is tested in the case of space-invariant conditions by considering the reconstruction of simple on-axis cylindrical phantoms.     

Image restoration in computed tomography: restoration of experimental CT images

It is pointed out that to restore experimental computed tomography (CT) images which have been blurred by a spatially variant point spread function (PSF), a quadrant symmetry method which simultaneously optimizes storage requirements for the estimated PSFs and computational speed is used. The quadrant symmetry approach required less than 0.579 Megawords of storage for 9x9 pixel spatially variant PSFs in a 256x256 pixel image, allowing image restoration in Cartesian coordinates. A locally adaptive restoration method formulated using a noise prewhitening filter derived from the measured noise power spectrum, the conjugate gradient method was used to obtain a numerical solution. Restored images for a resolution phantom and for a cadaver femur are presented.     

Image restoration by aperture-correction techniques

Improvement of television pictures de by aperture effects is at present performed by horizontal and vertical aperture correctors operating on the video signal. It is shown that true restoration can be more closely approximated to by a modified arrangement, and the limitations imposed by spatial noise are investigated.     

Image restoration by regularisation in uncorrelated transformdomain

Conventional spatially adaptive regularised image restoration schemes weight the amount of regularisation according to the spatial content of an image. The authors first separately decorrelate the signals under analysis into uncorrelated components and then weight the amount of regularisation performed to these components accordingly. The proposed approach works better than conventional schemes, especially in edge regions     

基于微分近似的图像修复

图像修复是对图像的破损区域进行填充,主要是利用待修复边缘信息对待修复区域进行填充。现有多数图像修复算法都非常复杂,而且难于实现。文中提出了一种基于微分近似、利用图像的空间连续性进行图像修复的算法。该算法实现简单,修补速度快,对小区域的修复效果较好。     

Image restoration using the W-slice method

We propose the use of higher order statistics (HOS)-based methods to address the problem of image restoration. The restoration strategy is based on the fact that the phase information of the original image and its HOS are not distorted by some types of blurring. The difficulties associated with the combination of 2-D signals and their HOS are reduced by means of the Radon transform. Two methods that apply the weight-slice algorithm over the projections are developed. Simulation results illustrate the performance of the proposed methods.     

Image power in a microwave crystal mixer

An investigation of the output of a microwave crystal mixer at image frequency is described. A curve is presented showing the dependence of image output on i.f. termination. This curve indicates an operating region where mixer performance is insensitive to image termination.     

Image identification and restoration in the subband domain

When faced with a large support point spread function (PSF), the iterative expectation-maximization (EM) algorithm, which is often used for PSF identification, is very sensitive to the initial PSF estimate. To deal with this problem, the authors propose to do EM image identification and restoration in the subband domain. After the image is first divided into subbands, the EM algorithm is applied to each subband separately. Since the PSF can be taken to have smaller support in each subband, these subbands should be less of a problem with the EM model identification. They also introduce an adaptive subband EM method for use in the upper frequency subbands.     

Image restoration in astronomy: a Bayesian perspective

When preparing an article on image restoration in astronomy, it is obvious that some topics have to be dropped to keep the work at reasonable length. We have decided to concentrate on image and noise models and on the algorithms to find the restoration. Topics like parameter estimation and stopping rules are also commented on. We start by describing the Bayesian paradigm and then proceed to study the noise and blur models used by the astronomical community. Then the prior models used to restore astronomical images are examined. We describe the algorithms used to find the restoration for the most common combinations of degradation and image models. Then we comment on important issues such as acceleration of algorithms, stopping rules, and parameter estimation. We also comment on the huge amount of information available to, and made available by, the astronomical community     

一种基于神经网络的图像复原方法

提出了一种基于BP神经网络的图像复原算法.在分析图像模糊机制的基础上,为了降低输入维数,该方法采用滑动窗口操作来提取特征,同时为了加快训练速度和改善网络复原效果,首先对图像进行边缘提取,对图像内边缘区域和平坦区域分别采用滑动窗口获得训练集.利用BP神经网络的学习能力,通过训练,建立含有退化信息(高斯模糊)的模糊图像和清晰图像之间的映射关系模型,利用该模型对模糊图像进行复原,得到的复原图像在视觉上和定量分析上都获得了较好的效果.     

基于非局部全变差模型的图像复原方法

面向图像复原问题,本文提出一种非局部的全变差图像复原方法。该方法将传统的全变差模型拓展为非局部变差模型,充分利用非局部子块对结构的保持作用,进一步提高复原图像的质量。此外,为了解决上述的非局部全变差模型,本文引入算子运算简化目标函数, 再利用迭代的Splitting算法对其进行交替求解,提高收敛精度。实验结果表明,本文算法在视觉效果和客观评价指标两方面均优于传统算法。     

Image interpretation and prediction in microwave diversity imaging

The microwave image of a metallic object is interpreted from a point of view based on the understanding of the interconnection between the scattering mechanisms, the data acquisition system, and the image reconstruction algorithm. From this understanding it is possible to interpret and predict microwave images reconstructed from data collected over specified and angular windows. The connection between a special scattering mechanism, edge diffraction, and its reconstructed image is established. The microwave image of an edge is two bright points whose locations correspond to the end points of the edge if the normal aspect angle is not included in the angular windows; otherwise a line joining the two end points and representing the edge will appear in the image. Experimental images of a trihedral reflector constructed from data collected over different angular windows support this approach to image interpretation and prediction     

基于图像分解的图像修复算法

针对传统变分模型在修复图像时易产生“阶梯效应”与细节模糊等问题,提出了一种基于图像分解的自适应二阶总广义变分和分数阶变分的图像修复算法。首先将待修复的目标图像分解为卡通部分与纹理部分,其中卡通对应目标图像的低、中频部分,因此利用抑制“阶梯效应”较好的二阶总广义变分模型对其进行修复;纹理对应其高频部分,因此利用对细节部分有增强效果的分数阶变分模型对其进行修复。由于文中所提到的修复模型均与线性鞍点结构下求取最优值的模型类似,因此在算法上均采用基于预解式的原始对偶算法对新模型进行求解。另外,为了取得更好的修复效果,文中设计了一个边缘指示算子来自适应地控制新模型的扩散,以更好地保护修复图像的边缘细节。实验结果表明:相比传统的TV、TGV修复模型,新模型的修复效果在主观视觉上显得更加自然,且在峰值信噪比与相关系数等客观评价指标上均有提高。     

Image restoration using a modified Hopfield network

A modified Hopfield neural network model for regularized image restoration is presented. The proposed network allows negative autoconnections for each neuron. A set of algorithms using the proposed neural network model is presented, with various updating modes: sequential updates; n-simultaneous updates; and partially asynchronous updates. The sequential algorithm is shown to converge to a local minimum of the energy function after a finite number of iterations. Since an algorithm which updates all n neurons simultaneously is not guaranteed to converge, a modified algorithm is presented, which is called a greedy algorithm. Although the greedy algorithm is not guaranteed to converge to a local minimum, the l (1) norm of the residual at a fixed point is bounded. A partially asynchronous algorithm is presented, which allows a neuron to have a bounded time delay to communicate with other neurons. Such an algorithm can eliminate the synchronization overhead of synchronous algorithms.     

Image selective restoration using multi-scale variational decomposition

In this paper we propose a multi-scale variational decomposition model for image selective restoration. Firstly, we introduce a single-parameter (BV, G, L²) variational decomposition functional and theoretically analyze the relationship between the parameter and the scale of image features. And then, by replacing the fixed scale parameter with a varying sequence in the single-parameter decomposition functional, we obtain the multi-scale variational decomposition which can decompose the input image into a series of image slices of different scales. Furthermore, we show some properties and prove the convergence of the multi-scale decomposition. Finally, we introduce an alternating and iterative method based on Chambolle’s projection algorithm to numerically solve the multi-scale variational decomposition model. Experiments are conducted on both synthetic and real images to demonstrate the effectiveness of the proposed multi-scale variational decomposition. In addition, we use the multi-scale variational decomposition to achieve image selective restoration, and compare it with several state-of-the-art models in denoising application. The numerical results show that our model has better performance in terms of PSNR and SSIM indexes.     

基于非盲去模糊算法的图像复原

在图像复原中,对降质图像失真的点扩散函数求逆是研究难点之一。现根据先验概率将贝叶斯理论的函数关系化,提出遗传算法的种群繁殖模型,通过逐步演化来执行变换、调整和集成。同时,为了获取更高效的效果,构建Jenkins持续集成平台进行集成和测试模型参数。最后,从决策支持系统的角度阐述整个系统的框架。     

Image processing for virtual restoration of artworks

As tools for artwork restoration, image-processing techniques serve two purposes. They can be used as a guide to the actual restoration of the artwork (computer-guided restoration). Or, they can produce a digitally restored version of the work, which itself is valuable, although the restoration is only virtual and cannot be reproduced on the real piece of work (virtual restoration). We present two applications of digital image processing for restoring artworks. The first belongs to the class of computer-guided restoration techniques, and the second represents an example of virtual artwork restoration. The first example refers to cleaning dirty paintings. More specifically, we describe a technique that, by relying on cleaning a small patch of the painting, can foresee the final result when the same cleaning methodology is applied to the whole piece of work. Restorers can use it by applying a set of different cleaning methodologies to very small patches of the painting. They can then use the virtual cleaning software to determine which cleaning procedure is likely to give the best result, thus using digital image processing as a tool to guide the actual restoration of the artwork. In our second example, we present a system for removing cracks from old paintings and frescos. In many cases, cracks severely deteriorate the aspect of paintings both because of their number and their heaviness. Thus, a system capable of removing them is of great interest, even if the removal is only virtual     

基于小波去噪的T-ray图像复原

针对实时THz脉冲（T-ray）成像系统所成图像分辨率低、受l/f相关噪声干扰严重的特点,提出一种新的基于小波去噪的T—ray图像复原算法。对T-ray图像进行离散小波变换后,先利用广义交叉确认估计出各个分辨率层的噪声闽值,然后对每个分辨率层的高频子带进行迭代去噪,最后对去噪后的T-ray图像采用Jasson-Van-Cittert算法进行复原处理以提高分辨率。实验结果表明,该方法在提高T-ray图像分辨率的同时,能显著地抑制THz成像系统的l/f相关噪声。创新之处在于以广义交叉确认作为T-ray图像中l/f噪声的估计方法,大幅度提高了图像信噪比（-5dB）,避免了噪声带来的复原算法中的不适定问题,达到较好的图像复原效果。     

X射线图像和CT图像的多维配准

在本文中,我们提出了一种基于Chamfer3-4距离变换和Powell优化的方法,用于X射线图像和CT图像间的配准.该方法首先从X射线图像中分割出目标物体.通过使用投影模型和优化方法,得到了准确的投影矩阵.该方法同样被用于医学介入手术中,处理从三维图像工作站中获取的主动漫游的数据.