A local update strategy for iterative reconstruction fromprojections

A method for Bayesian reconstruction which relies on updates of single pixel values, rather than the entire image, at each iteration is presented. The technique is similar to Gauss-Seidel (GS) iteration for the solution of differential equations on finite grids. The computational cost per iteration of the GS approach is found to be approximately equal to that of gradient methods. For continuously valued images, GS is found to have significantly better convergence at modes representing high spatial frequencies. In addition, GS is well suited to segmentation when the image is constrained to be discretely valued. It is shown that Bayesian segmentation using GS iteration produces useful estimates at much lower signal-to-noise ratios than required for continuously valued reconstruction. The convergence properties of gradient ascent and GS for reconstruction from integral projections are analyzed, and simulations of both maximum-likelihood and maximum a posteriori cases are included     

基于转向核的单帧红外条纹非均匀性拟合校正算法

针对红外线列和非制冷型焦平面成像系统存在列向条纹非均匀性的现象,提出了一种基于转向核的单帧条纹非均匀性校正新算法。首先,根据图像边缘的梯度特性确定转向核邻域主方向和大小,然后将每列像素的转向核估计值作为该列像素的期望值,以列向条纹非均匀性作为约束进行最小二乘拟合,计算得到每列像元的校正参数,并以一定概率接受最小二乘拟合的结果。当误差函数满足事先设定阈值时,单帧图像的非均匀性校正完成。实验结果表明,该算法具有稳定的收敛性,与同类算法相比能够更有效抑制条纹非均匀性,并且能够保留更多的图像边缘信息。     

基于包围盒约束光谱聚类的红外目标识别算法

在红外成像过程中,目标边缘模糊化是影响红外目标识别效果的关键因素,也是红外目标识别算法的研究重点,故在光谱图像中合理补偿目标几何特征信息成为研究热点之一。结合包含目标几何特征信息的包围盒作为约束条件,对红外光谱图像进行分层限定滤波,降低原有图像数据中目标几何外形数据的丢失,提高目标可识别性。设计了在包围盒约束条件下的光谱聚类算法,设置参数η表征待测军用车辆目标的几何信息,设置参数m表征待测军用车辆目标的光谱特征信息。实验采用TEL-1000-MW型红外成像光谱仪获取多光谱图像,通过改变m和η值调整光谱特征值个数与包围盒范围,从而获得不同的目标识别图像。并与传统方法对同一幅红外目标图像的识别效果相比较,结果发现采用包围盒约束的待测目标图像几何边界信息保留效果明显优于传统方法,当m=10、η=0.7时,红外图像的目标识别效果最好,同时算法收敛速度也最优。由此可见,该算法在提高红外目标识别能力、避免误判伪目标和漏检目标方面具有很高的实用价值。     

General choice of the regularization functional in regularizedimage restoration

The determination of the regularization parameter is an important issue in regularized image restoration, since it controls the trade-off between fidelity to the data and smoothness of the solution. A number of approaches have been developed in determining this parameter. In this paper, a new paradigm is adopted, according to which the required prior information is extracted from the available data at the previous iteration step, i.e., the partially restored image at each step. We propose the use of a regularization functional instead of a constant regularization parameter. The properties such a regularization functional should satisfy are investigated, and two specific forms of it are proposed. An iterative algorithm is proposed for obtaining a restored image. The regularization functional is defined in terms of the restored image at each iteration step, therefore allowing for the simultaneous determination of its value and the restoration of the degraded image. Both proposed iteration adaptive regularization functionals are shown to result in a smoothing functional with a global minimum, so that its iterative optimization does not depend on the initial conditions. The convergence of the algorithm is established and experimental results are shown.     

引入多状态记忆机制的迭代软阈值学习算法及其应用

迭代软阈值学习算法(Learned Iterative Soft-Thresholding Algorithm,LISTA)将迭代软阈值算法(Iterative Soft-Thresholding Algorithm,ISTA)展开为递归前馈神经网络优化稀疏恢复的求解。针对LISTA单次迭代只依赖于前一迭代点限制算法收敛速率的问题,本文提出了一种引入多状态记忆机制的迭代软阈值学习算法(Learned Iterative Soft-Thresholding Algorithm with Multi-state Memory Mechanism,LISTA-MM)。该算法基于一阶迭代固定步长算法对LISTA进行改进,设置状态连接度数,选择性地组合多个先前迭代点的稀疏信息,确保了迭代过程中信息被正确传递并充分利用,进而加快了算法的收敛速度。实验结果表明,LISTA-MM在保证稀疏恢复精度的同时有效提高了收敛速度。此外,本文将LISTA-MM扩展为卷积形式,并探索其在图像超分辨率中的应用,实验结果表明,基于LISTA-MM的网络在图像质量评价指标和可视化效果上均优于其他网络,重构图像具有与原始图像相近的清晰细节纹理。      

Simultaneous iterative image restoration and evaluation of theregularization parameter

A nonlinear regularized iterative image restoration algorithm is proposed, according to which only the noise variance is assumed to be known in advance. The algorithm results from a set theoretic regularization approach, where a bound of the stabilizing functional, and therefore the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the algorithm are derived and experimental results are shown     

Maximum a posteriori spatial probability segmentation

An image segmentation algorithm that performs pixel-by-pixel segmentation on an image with consideration of the spatial information is described. The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probability that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a posteriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such that each pixel is segmented into a particular class when the a posteriori spatial probability is a maximum. The proposed segmentation algorithm is implemented in an iterative form. During the iteration, a series of intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure is chosen as the optimum segmented image. Results from segmenting synthetic and practical images demonstrate that the MASP algorithm is capable of achieving better results when compared with other global thresholding methods     

An enhanced NAS-RIF algorithm for blind image deconvolution

We enhance the performance of the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image deconvolution. The original cost function is modified to overcome the problem of operation on images with different scales for the representation of pixel intensity levels. Algorithm resetting is used to enhance the convergence of the conjugate gradient algorithm. A simple pixel classification approach is used to automate the selection of the support constraint. The performance of the resulting enhanced NAS-RIF algorithm is demonstrated on various images.     

A fast reconstruction algorthm for stationary positron emission tomography based on a modified em algorithm

An efficient iterative reconstruction method for positron emission tomography (PET) is presented. The algorithm is basically an enhanced EM (expectation maximization) algorithm with improved frequency response. High-frequency components of the ratio of measured to calculated projections are extracted and are taken into account for the iterative correction of image density in such a way that the correction is performed with a uniform efficiency over the image plane and with a flat frequency response. As a result, the convergence speed is not so sensitive to the image pattern or matrix size as the standard EM algorithm, and nonuniformity of the spatial resolution is significantly improved. Nonnegativity of the reconstructed image is preserved. Simulation studies have been made assuming two PET systems: a scanning PET with ideal sampling and a stationary PET with sparse sampling. In the latter, a "bank array" of detectors is employed to improve the sampling in the object plane. The new algorithm provides satisfactory images by two or three iterations starting from a flat image in either case. The behavior of convergence is monitored by evaluating the root mean square of C(b)-1 where C(b) is the correction factor for pixel b in the EM algorithm. The value decreases rapidly and monotonically with iteration number. Although the theory is not accurate enough to assure the stability of convergence, the algorithm is promising to achieve significant saving in computation compared to the standard EM algorithm.     

基于增量维纳滤波和空间自适应规整的超分辨率图像复原

该文提出一种新的超分辨率图像复原方案,该方案利用了空间自适应规整方法的空间分段平滑特性和增量维纳滤波的快速收敛能力,将解的先验约束结合到迭代过程中,通过多幅低分辨率降晰图像来估计一幅高分辨率图像。计算机仿真结果表明,该方法可以有效实现超分辨率图像复原,有效地提高了复原的自适应控制能力和收敛性能。     

一种自适应PCNN多聚焦图像融合新方法

该文通过分析脉冲耦合神经网络(PCNN)参数模型,结合多聚焦图像的基本特点和人眼视觉特性,提出了一种自适应PCNN多聚焦图像融合的新方法。该方法使用图像逐像素的清晰度作为PCNN对应神经元的链接强度,经过PCNN点火获得每幅参与融合图像的点火映射图,再通过判决选择算子,判定并选择各参与融合图像中的清晰部分生成融合图像。该方法中,其它参数如阈值调整常量等对于融合结果影响很小,解决了PCNN方法的参数调整困难的问题。实验结果表明,该方法的融合效果优于小波变换方法和Laplace塔型方法。     

二层减法聚类图像颜色迁移算法

提出了一种图像自动颜色迁移方法,利用二层减法聚类将图像分成一定数量的子块,通过灰度差值直方图方法提取子块纹理特征,建立目标图像与源图像间子块的最佳对应关系,选取各子块数据点密度较大的像素组成子块的样本块,在对应源图像样本块中查找目标样本块的最佳匹配像素,完成样本块间颜色迁移,进而根据已上色的样本块完成目标图像的全局颜色迁移.该算法既适用于彩色图像间颜色迁移也适用于灰度图像上彩色.     

Reversible data-hiding for progressive image transmission

This research presents a multi-resolution reversible data-hiding algorithm to enable multi-scale marked images that are transmitted progressively to be exactly recovered at the receiver side once hidden data has been extracted. Based on the spatially hierarchical multi-layer structures of progressive-image transmission, the proposed algorithm first decimates the incoming image pixels into a pre-specified number of hierarchical layers of pixels. Then, it modifies pixel values in each hierarchical layer by shifting the interpolated-difference-values histogram between two neighboring layers of pixels to embed secret information into the corresponding hierarchical layer images. The proposed algorithm offers a reversible data-hiding ability for applications that use progressive image transmission to render progressive-image authentication, information-tagging, covert communications, etc. With progressive-reversible data-hiding, users of progressive image transmission can receive each original progressive image and complete hidden messages related to the received progressive image. This allows users to make real-time definite decisions according to an application's requirements. In contrast to other reversible data-hiding schemes, the algorithm proposed in this study features reversible data-hiding in progressive-image transmission based on a hierarchical decimation and interpolation technique. The interpolating process is used to reduce the difference values between the target pixel values in one progressive layer and their interpolated ones. This increases the hiding capacity of interpolation-differences histogram shifting. The experimental results demonstrate that the proposed method provides a greater embedding capacity and maintains marked images at a higher quality. Moreover, the proposed method has a low computational complexity as it requires only simple arithmetic computations.     

A new fuzzy-based decision algorithm for high-density impulse noise removal

This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).     

空间变化离焦模糊红外图像快速复原算法

为了提升空间变化离焦模糊红外图像的图像质量,提出了一种基于图像质量评价的快速复原算法.本文提出的方法首先对模糊图像采用不同点扩散函数对应的截断约束最小二乘法算法进行复原而获得多幅复原图像,并对复原图像进行去振铃;然后对复原图像中每个像素为中心的区域进行图像质量评价,将采用不同参数复原的图像以图像质量评价的结果进行组合以...     

A quadratically convergent algorithm for convex-set constrainedsignal recovery

This paper addresses the problem of recovering a signal that is constrained to lie in a convex set, from linear measurements. The current standard is the alternating projections paradigm (POCS), which has only first-order convergence in general. We present a quadratically convergent iterative algorithm (Newton algorithm) for signal recovery from linear measurements and convex-set constraints. A new result on the existence and construction of the derivative of the projection operator onto a convex set is obtained, which is used in the Newton algorithm. An interesting feature of the new algorithm is that each iteration requires the solution of a simpler subspace-constrained reconstruction problem. A computation- and memory-efficient version of the algorithm is also obtained by using the conjugate-gradient algorithm within each Newton iteration to avoid matrix inversion and storage. From a computational point of view, the computation per iteration of this algorithm is similar to the computation per iteration of the standard alternating projections algorithm. The faster rate of convergence (compared to alternating projections) enables us to obtain a high-resolution reconstruction with fewer computations. The algorithm is thus well suited for large-scale problems that typically arise in image recovery applications. The algorithm is demonstrated in several applications     

一种基于压缩感知的迭代重建算法

迭代重建算法是一种经典的CT图像重建算法,适合于不完全投影数据的图像重建,其缺点是重建速度慢。为提高图像重建的质量和速度,文中利用压缩感知理论提出了一种改进的基于图像全变差最小的迭代重建算法。该算法在迭代的不同阶段对迭代初始值做不同处理,并在每次迭代结束后采用梯度下降法调整全变差。实验结果表明,该算法不但提高了图像重建质量,同时也加快了迭代图像的收敛速度。     

Image Restoration by Lifting‐Based Wavelet Domain E‐Median Filter

In this paper, we propose a method of applying a lifting‐based wavelet domain e‐median filter (LBWDEMF) for image restoration. LBWDEMF helps in reducing the number of computations. An e‐median filter is a type of modified median filter that processes each pixel of the output of a standard median filter in a binary manner, keeping the output of the median filter unchanged or replacing it with the original pixel value. Binary decision‐making is controlled by comparing the absolute difference of the median filter output and the original image to a preset threshold. In addition, the advantage of LBWDEMF is that probabilities of encountering root images are spread over sub‐band images, and therefore the e‐median filter is unlikely to encounter root images at an early stage of iterations and generates a better result as iteration increases. The proposed method transforms an image into the wavelet domain using lifting‐based wavelet filters, then applies an e‐median filter in the wavelet domain, transforms the result into the spatial domain, and finally goes through one spatial domain e‐median filter to produce the final restored image. Moreover, in order to validate the effectiveness of the proposed method we compare the result obtained using the proposed method to those using a spatial domain median filter (SDMF), spatial domain e‐median filter (SDEMF), and wavelet thresholding method. Experimental results show that the proposed method is superior to SDMF, SDEMF, and wavelet thresholding in terms of image restoration.     

一种基于新超混沌Chen系统的置乱替代相结合的加密算法

随着多元化媒介和数字化信息网络的急速发展,数码形象加密技术在图形形象的安全保存、传达输送、著作权保护和秘密通信等领域被普遍推广应用。针对现有基于超级混沌的图像加密算法的缺点,提出了一种改进算法,该算法对像素加扰进行优化配置,通过像素置换和加密文本扩散过程,进一步混乱明文图像与加密文本图像的关联效应,从而能缩短超级混沌系统的迭代时间。研究结果表明加密后直方图的像素值分布均匀;密文之间的NPCR值和UACI值分别为99.6521%和33.4321%,表明算法对加密密钥的微小改变具有极强的敏感性;在新超混沌序列量化模式中引入该方法可有效提高操作效率,且该算法无论在安全方面还是有效运用方面都具备良好性能,可在图像安全通信和其他领域广泛使用。