A cartoon-plus-texture image decomposition model for blind deconvolution

In this paper, we study a blind deconvolution problem by using an image decomposition technique. Our idea is to make use of a cartoon-plus-texture image decomposition procedure into the deconvolution problem. Because cartoon and texture components can be represented differently in images, we can adapt suitable regularization methods to restore their components. In particular, the total variational regularization is used to describe the cartoon component, and Meyer’s G-norm is employed to model the texture component. In order to obtain the restored image automatically, we also use the generalized cross validation method efficiently and effectively to estimate their corresponding regularization parameters. Experimental results are reported to demonstrate that the visual quality of restored images by using the proposed method is very good, and is competitive with the other testing methods.     

基于子空间分解的多通道盲解卷积算法

针对卷积混合信号,提出了一种新的多通道盲解卷积算法,该算法首先利用子空间分解方法,将信号卷积混合模型变换成线性混合模型,然后利用线性混合盲分离算法分离出源信号.该算法相对频域盲解卷积算法来说无需解决线性混合盲分离中存在的幅度和排列顺序的模糊性问题,而且该算法不要求信号独立同分布,只要求各源信号统计独立即可.因此,该算法可以直接在中频对观察信号进行处理.计算机仿真结果表明,该算法不仅能对不同频不同调制方式的通信信号进行盲解卷积,而且对同频同调制的通信信号,该算法同样有效.     

The application of blind channel identification techniques toprestack seismic deconvolution

One objective of seismic signal processing is to identify the layered subsurface structure by sending seismic wavelets into the ground. This is a blind deconvolution process since the seismic wavelets are usually not measurable and therefore, the subsurface face layers are identified only by the reflected seismic signals. Conventional methods often approach this problem by making assumptions about the subsurface structures and/or the seismic wavelets. In this paper an alternative technique is presented. It applies blind channel identification methods to prestack seismic deconvolution. A unique feature of this proposed method is that no such assumptions are needed. In addition, it fits into the structure of current seismic data acquisition techniques, thus no extra cost is involved. Simulations on both synthetic and field seismic data demonstrate that it is a promising new method for seismic signal processing     

基于低秩稀疏分解的湍流退化图像序列的盲去卷积算法

针对湍流退化图像序列存在像偏移、像抖动和像 模糊的问题,提出一种基于低秩稀疏分解和多帧去 卷积的图像复原算法。首先分析大气湍流下图像序列的退化特征,然后在低秩稀疏分解的思 想下,采用非增广拉格朗日乘子(IALM)法优化由低秩 矩阵的核范数和稀疏 矩阵的Frobenius范数之和构成的目标函数,将湍流退化序列分解为低秩稳像和稀疏湍流两 部分;最后利用 多帧去卷积算法复原对齐的稳像。实验结果表明,本文算法能够有效校 正湍流像素偏移,在提高复原质量和速度方面取得了明显的效果。     

Maximum-likelihood blind deconvolution: non-whiteBernoulli-Gaussian case

The authors present a maximum-likelihood deconvolution (MLD) algorithm for estimating nonwhite Bernoulli-Gaussian signals μ(k ), which were distorted by a linear time-invariant system υ( k) taking into account the measured spectrum of μ(k) such as that obtained from sonic logs. The proposed MLD algorithm can recover both the phase of a minimum-phase coloring filter υ₁(k) and that of υ(k) as long as the spectrum of μ(k) is known in advance. The authors also present some simulation results which support the proposed MLD algorithm     

Optimal seismic deconvolution: distributed algorithms

Deconvolution is one of the most important aspects of seismic signal processing. The objective of the deconvolution procedure is to remove the obscuring effect of the wavelet's replica making up the seismic trace and therefore obtain an estimate of the reflection coefficient sequence. This paper introduces a new deconvolution algorithm. Optimal distributed estimators and smoothers are utilized in the proposed solution. The new distributed methodology, perfectly suitable for a multisensor environment, such as the seismic signal processing, is compared to the centralized approach, with respect to computational complexity and architectural efficiency. It is shown that the distributed approach greatly outperforms the currently used centralized methodology offering flexibility in the design of the data fusion network     

New criteria for blind deconvolution of nonminimum phase systems(channels)

A necessary and sufficient condition for blind deconvolution (without observing the input) of nonminimum-phase linear time-invariant systems (channels) is derived. Based on this condition, several optimization criteria are proposed, and their solution is shown to correspond to the desired response. These criteria involve the computation only of second- and fourth-order moments, implying a simple tap update procedure. The proposed methods are universal in the sense that they do not impose any restrictions on the probability distribution of the (unobserved) input sequence. It is shown that in several important cases (e.g. when the additive noise is Gaussian), the proposed criteria are essentially unaffected     

An adaptive two phase blind image deconvolution algorithm for an iterative regularization model

This paper proposes a blind image deconvolution method which consists of two sequential phases, i.e., blur kernel estimation and image restoration. In the first phase, we adopt the L₀-norm of image gradients and total variation (TV) to regularize the latent image and blur kernel, respectively. Then we design an alternating optimization algorithm which jointly incorporates the estimation of intermediately restored image, blur kernel and regularization parameters into account. In the second phase, we propose to take the mixture of L₀-norm of image gradients and TV to regularize the latent image, and design an efficient non-blind deconvolution algorithm to achieve the restored image. Experimental results on both a benchmark image dataset and real-world blurred images show that the proposed method can effectively restore image details while suppress noise and ringing artifacts, the result is of high quality which is competitive with some state of the art methods.     

A new algorithm of blind color image watermarking based on LU decomposition

This paper proposes a new algorithm of blind color image watermarking based on LU decomposition. Because of the first column the second row element and the first column the third row element of the lower triangular matrix, which obtained by LU decomposition, have higher similarity, and the color image watermark can be embedded into these elements by slightly modifying rules, which can enhance the watermark invisibility. In addition, Arnold transform is applied to improve the watermark security and the Hash pseudo-random number algorithm based on MD5 is used to improve the watermark robustness. In extraction process, the watermark embedding strength and private key are needed to extract watermark information from the attacked host image with blind manner. Experiment results reveal that the proposed method outperforms other related methods in the aspects of the invisibility, robustness, embedding payload and computational complexity.     

Multichannel blind image deconvolution using the Bussgang algorithm: spatial and multiresolution approaches

This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems. We address the restoration of images with poor spatial correlation as well as strongly correlated (natural) images. The spatial nonlinearity employed in the final estimation step of the Bussgang algorithm is developed according to the minimum mean square error criterion in the case of spatially uncorrelated images. For spatially correlated images, the nonlinearity design is rather conducted using a particular wavelet decomposition that, detecting lines, edges, and higher order structures, carries out a task analogous to those of the (preattentive) stage of the human visual system. Experimental results pertaining to restoration of motion blurred text images, out-of-focus spiky images, and blurred natural images are reported.     

盲目反卷积算法在高超流场星图复原中的应用

近年来高超声速飞行器的研究受到世界各国的重视,具有重大的军事意义,其中导航、制导和控制是高超声速研究的关键技术。鉴于星光导航有着抗干扰能力强、导航精度高、自助式导航等特点,文中主要研究了高超声速飞行器使用星光导航方法的星图复原算法。考虑到高超带来的气动光学效应,在分析高速层流以及湍流流场的基础上,应用增量Wiener滤波器和有限支持域上的盲解卷积复原算法进行退化星图的复原。针对高速飞行器星光导航对复原星图的要求,仿真分析了复原星图的质心偏差及识别特征量变化。仿真结果显示,有限支持域上的盲解卷积复原算法精度较高,且经复原后的星图,能快速被高速飞行器星光导航系统正确识别。     

Self-learning based image decomposition for blind periodic noise estimation: a dual-domain optimization approach

Periodic noise reduction is a fundamental problem in image processing, which severely affects the visual quality and subsequent application of the data. Most of the conventional approaches are only dedicated to either the frequency or spatial domain. In this research, we propose a dual-domain approach by converting the periodic noise reduction task into an image decomposition problem. We introduced a bio-inspired computational model to separate the original image from the noise pattern without having any a priori knowledge about its structure or statistics. From the filtering perspective, the proposed method filters out only a portion of the noisy frequencies. Some considerations have to be taken into account for computational resources (computing time and memory space) which permits reducing computation complexity without sacrificing the quality of the image reconstruction. In addition, the separator size provided in the decomposition algorithm does not depend on the image size. Experiments on both synthetic and non-synthetic noisy images have been carried out to validate the effectiveness and efficiency of the proposed algorithm. The simulation results demonstrate the effectiveness of the proposed method both qualitatively and quantitatively.

     

Measured equation of invariance: a new concept in fieldcomputations

Numerical computations of frequency domain field problems or elliptical partial differential equations may be based on differential equations or integral equations. The new concept of field computation presented in this paper is based on the postulate of the existence of linear equations of the discretized nodal values of the fields, different from the conventional equations, but leading to the same solutions. The postulated equations are local and invariant to excitation. It is shown how the equations can be determined by a sequence of “measures”. The measured equations are particularly useful at the mesh boundary, where the finite difference methods fail. The measured equations do not assume the physical condition of absorption, so they are also applicable to concave boundaries. Using the measured equations, one can terminate the finite difference mesh very close to the physical boundary and still obtain robust solutions. It will definitely make a great impact on the way one applies finite difference and finite element methods in many problems. Computational results using the measured equations of invariance in two and three dimensions are presented     

A novel approach to the 2-D blind deconvolution problem in medical ultrasound

The finite frequency bandwidth of ultrasound transducers and the nonnegligible width of transmitted acoustic beams are the most significant factors that limit the resolution of medical ultrasound imaging. Consequently, in order to recover diagnostically important image details, obscured due to the resolution limitations, an image restoration procedure should be applied. The present study addresses the problem of ultrasound image restoration by means of the blind-deconvolution techniques. Given an acquired ultrasound image, algorithms of this kind perform either concurrent or successive estimation of the point-spread function (PSF) of the imaging system and the original image. In this paper, a blind-deconvolution algorithm is proposed, in which the PSF is recovered as a preliminary stage of the restoration problem. As the accuracy of this estimation affects all the following stages of the image restoration, it is considered as the most fundamental and important problem. The contribution of the present study is twofold. First, it introduces a novel approach to the problem of estimating the PSF, which is based on a generalization of several fundamental concepts of the homomorphic deconvolution. It is shown that a useful estimate of the spectrum of the PSF can be obtained by applying a proper smoothing operator to both log-magnitude and phase of the spectra of acquired radio-frequency (RF) images. It is demonstrated that the proposed approach performs considerably better than the existing homomorphic (cepstrum-based) deconvolution methods. Second, the study shows that given a reliable estimate of the PSF, it is possible to deconvolve it out of the RF-image and obtain an estimate of the true tissue reflectivity function, which is relatively independent of the properties of the imaging system. The deconvolution was performed using the maximum a-posteriori (MAP) estimation framework for a number of statistical priors assumed for the reflectivity function. It is shown in a series of in vivo experiments that reconstructions based on the priors, which tend to emphasize the "sparseness" of the tissue structure, result in solutions of higher resolution and contrast.     

Understanding phase maps in MRI: a new cutline phase unwrapping method

This paper describes phase maps. A review of the phase unwrapping problem is given. Different structures, in particular fringelines, cutlines, and poles, contained within a phase map are described and their origin and behavior investigated. The problem of phase unwrapping can then be addressed with a better understanding of the source of poles or inconsistencies. This understanding, along with some assumptions about what is being encoded in the phase of a magnetic resonance image, are used to derive a new method for phase unwrapping which relies only on the phase map. The method detects cutlines and distinguishes between noise-induced poles and signal undersampling poles based on the length of the fringelines. The method was shown to be robust to noise and successful in unwrapping challenging clinical cases.     

Planim: a new concept in the design of MIC filters

The planar circuit approach for the characterisation of microstrip circuits is combined with the image parameter method to give a new powerful technique for integrated circuit filter design (planim). The inclusion of two-dimensional effects in the synthesis procedure makes the planim approach particularly suited for monolithic microwave circuits. The design of microstrip filters with extremely wide stopbands is shown as an example.     

Describing fuzzy sets using a new concept: fuzzify functor

This paper proposed a fuzzify functor as an extension of the concept of fuzzy sets.The fuzzify functor and the first-order operated fuzzy set are defined.From the theory analysis,it can be observed that when the fuzzify functor acts on a simple crisp set,we get the first order fuzzy set or type-1 fuzzy set.By operating the fuzzify functor on fuzzy sets,we get the higher order fuzzy sets or higher type fuzzy sets and their membership functions.Using the fuzzify functor we can exactly describe the type-1 fuzz...     

Multimedia multi-networking: A new concept

Recent researches in communication systems are leading to the multiplication of communication technologies. Because of this trend there is now a very wide range of different kinds of networks from copper lines for telephony to high speed fibres, as well as satellite or wireless mobile networks. It would then be very useful to be capable of using all these new communication networks all together. We call this domain related to the use by an application of several different networks “multi-networking”. But the problematic of multi-networking is two folds: (1) First, it can be really interesting to have several network access and to be able to use them in parallel. For instance, it can consist, in the case of digital and interactive tv, of using digital satellite channels for broadcasting audio and video, and using the wire Internet or anIsdn (Integrated Services Digital Network) network to send specific data to dedicated users. This is what we call “parallel multi-networking”. (2) The second folder of this problematic deals with guaranteeing Quality of Service (QoS) while connections cross several networks or domains, especially when there are firewalls orNat (Network Address Translation) servers in between that break the end to end IP model. In addition, the introduction of wireless or satellite links that have high delays and loss ratio inside the Internet can lead to important QoS degradation as wire Internet protocols are not efficient on wireless and satellite links. This aspect of the problematic is called “serial multi-networking”. This second aspect has been much more addressed in a recent past than the first one. It leads to some specific solutions, most of the time application oriented, as caching for web application for instance. To cope with other problems, as the introduction of satellite links in the Internet, proxies system have been designed to handle data flows before entering the satellite link. Proxies are, there, in charge of performing some spoofing operations. But in any case, even if there are some application specific solutions, or some network dedicated approaches, none of them is able to handle live real time traffic. Hence, this work aims to propose a new solution relying on new protocols and architecture to cope with multi-networking. The solution for parallel multi-networking is called MMPOC-MN (MultiMedia Partial Order Connection for Multi-Networking). It is based on a partially ordered and reliable communication principle that allows us to reduce the end to end delay, and to enforce synchronisation between parallel flows on separated networks. This protocol can then be tuned very precisely in order to be the optimal transmission protocol according to application requirements and network constraints. To cope also with serial multi-networking, this protocol architecture has been extended. The new general (parallel and serial) multi-networking protocol is called MNP (Multi-Network Protocol). It is based on the concept of splitting the end to end connection in several trunks, each trunk being supported by a single network domain, each domain being supported by a single technology. Then, the best suited transmission parameters are used on each trunk, and the most suited spoofing algorithms are applied on data streams depending on the application requirement model. These protocols and architecture have been developed using the opnet modeller and simulated, to evaluate the benefits of our solution. In this paper we are also focusing on how deploying such applicative protocol and architecture. The recommended solution consists in using active networking as ANTS, capable to download and run portable code on network components.     

A NEW METHOD OF DECONVOLUTION IN THE FREQUENCY DOMAIN

A new deconvolution method has been presented in this paper. Like conventional Discrete Fourier Transform(DFT) method, it is also a frequency domain method. Since there are only DFT, IDFT and multiplication calculations in the new method, the singular problem appeared in the conventional DFT method can be avoid, and the deconvolution problem can always be solved with the new method. The relationship between the presented method and conventional DFT method is discussed. A numerical example is also presented to illustrate the new method.