基于点扩散函数支持域扩展的多帧未匹配退化图像盲解卷积复原

多帧盲解卷积算法利用多帧退化图像进行复原可以获得清晰原始图像和点扩散函数的信息,受到了很多研究者的关注。目前大部分多帧盲解卷积算法都需要对多帧退化图像进行匹配预处理,以消除图像间平移引入的算法求解误差。本文利用频率内的多帧盲解卷积算法对未匹配的退化图像进行处理,不需要进行预匹配处理,只需要对点扩散函数的支持域进行扩展就可以复原获取清晰化的图像。利用傅里叶变换的性质对该方法的可行性进行了说明。同时对该方法进行了数字仿真实验,复原结果中的点扩散函数发生了相对移动消除了图像间未匹配的影响,证实了本文方法的有效性。     

An envelope signal based deconvolution algorithm for ultrasound imaging

To improve the quality of medical ultrasound images, a number of restoration methods based on demodulated signals have been proposed in the literature. However, due to the shift of center frequency of transmitted ultrasound pulses at different penetration depth in a lossy medium, it is hard to determine the exact center frequency at a specified position so to achieve satisfactory demodulation. In this paper, this problem is dealt with by a novel restoration method based on envelope models of the radio frequency (RF) and the point spread function (PSF) signals. To cope with the ill inverse problem caused by the narrow band PSF, an envelop signal based sparse regularized deconvolution model is derived under a sparsity assumption of the tissue reflectivity function (TRF). Furthermore, a two-step iterative shrinkage/thresholding (TwIST) method based alternating minimization approach is applied to compute the optimal solution of the proposed deconvolution problem. Finally, the robustness and the practicability of the proposed method are demonstrated by a series of experiments on both numerical simulation and in vivo data. The experimental results show that the proposed method can achieve significant improvement of the ultrasound images in terms of the resolution gain and signal-to-noise ratio (SNR).     

Underwater image restoration by means of blind deconvolution approach

Although the use of blind deconvolution of image restoration is a widely known concept, little literatures have discussed in detail its application in the problem of restoration of underwater range-gated laser images. With the knowledge of the point spread function (PSF) and modulation transfer function (MTF) of water,underwater images can be better restored or enhanced. We first review image degradation process and Wells' small angle approximation theory, and then provide an image enhancement method for our underwater laser imaging system by blind deconvolution method based on small angle approximation. We also introduce a modified normalized mean square error (NMSE) method to validate the convergence of the blind deconvolution algorithm which is applied in our approach. The results of different initial guess of blind deconvolution are compared and discussed. Moreover, restoration results are obtained and discussed by intentionally changing the MTF parameters and using non-model-based PSF as the initial guess.     

Reply to "Comments on Geometric Deconvolution: A Meta-Algorithm for Limited View Computed Tomography"2

We have developed two new "meta-algorithms" for computed tomography that give significantly improved images through deconvolution of the two-dimensional point spread function of standard, quasi-linear algorithms. In geometric deconvolution the projections of the point spread function provide the basis for a set of one-dimensional deconvolutions. In two-dimensional Wiener deconvolution, the two-dimensional point spread function is deconvoluted directly. The criticism that there is no data available for these deconvolutions is met here by showing that the "missing data" is partly provided by incorporation of a priori information, as is the practice in other superresolution work.     

Digital processing for improvement of ultrasonic abdominal images

Digital processing that increases resolution by spatial deconvolution and histogram-based amplitude mapping has been used to improve ultrasonic abdominal image quality. The processing was applied to pulse-echo ultrasound data obtained from clinical imaging instrumentation modified to permit digital recording of signals in either RF or video forms for subsequent off-line analysis. Spatial deconvolution was accomplished both along the axis and across the width of the ultrasonic beam. Axial deconvolution was carried out on RF data with a point spread function derived from the echo of a wire target. Lateral deconvolution was performed on the video envelope placed in a matrix by an inverse filter with parameters that adjust themselves to the spatial frequency content of the image being processed. Resultant image amplitudes were mapped into a hyperbolic distribution to increase image contrast for improved demonstration of low amplitudes. The combination of processing produced resolution improvements to show boundaries more sharply and contrast changes to demonstrate more detail in the images.     

Multiple frame projection based blind deconvolution

Important a priori information available for blind deconvolution in problems such as astronomical imaging and remote sensing is the information in the multiple frame images in which the object is common for each frame but the point spread function varies. A projection based blind deconvolution algorithm for solving the multiple frame case is proposed     

A new multi-scale framework for convolutive blind source separation

This paper presents a new multi-scale decomposition algorithm which enables the blind separation of convolutely mixed images. The proposed algorithm uses a wavelet-based transform, called Adaptive Quincunx Lifting Scheme (AQLS), coupled with a geometric demixing algorithm called Deds. The resulting deconvolution process is made up of three steps. In the first step, the convolutely mixed images are decomposed by AQLS. Then, Deds is applied to the more relevant component to unmix the transformed images. The unmixed images are, thereafter, reconstructed using the inverse of the AQLS transform. Experiments carried out on images from various origins show the superiority of the proposed method over many widely used blind deconvolution algorithms.     

Gradient Adaptive Algorithms for Contrast-Based Blind Deconvolution

This paper presents extensions of stochastic gradient independent component analysis (ICA) methods to the blind deconvolution task. Of particular importance in these extensions are the constraints placed on the deconvolution system transfer function. While unit-norm constrained ICA approaches can be directly applied to the prewhitened blind deconvolution task, an allpass filter constraint within the optimization procedure is more appropriate. We show how such constraints can be approximately imposed within gradient adaptive finite-impulse-response (FIR) filter implementations by proper extensions of gradient techniques within the Stiefel manifold of orthonormal matrices. Both on-line time-domain and block-based frequency-domain algorithms are described. Simulations verify the superior performance behaviors provided by our allpass-constrained algorithms over standard unit-norm-constrained ICA algorithms in blind deconvolution tasks.     

空间扩展目标对准误差图像的高分辨复原

大气湍流、光子噪声和光学跟踪系统对准误差严重降低了空间目标观测图像的分辨率.根据最大似然估计原理,建立了提高目标图像分辨率的多帧盲反卷积算法,用共轭梯度优化方法从目标记录图像估计出原始目标函数和点扩散函数.运用低通平滑滤波技术在算法迭代过程中逐步完成对噪声的抑制.模拟实验数据和实际图像的复原结果表明,论文建立的盲反卷积算法有效地克服了大气湍流、光子噪声和光学系统对准误差,提高了目标图像的分辨率,复原目标图像的分辨率达到了光学衍射极限的水平.     

Semi-Blind Sparse Image Reconstruction With Application to MRFM

We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.     

Multidimensional system identification through blind deconvolution

By invoking characteristics of the recently introduced zero-sheet of the spectrum of a signal having finite (or compact) support, it is noted that the multidimensional system identification problem should be solvable through blind deconvolution, that is, the system response function should be inferrable in the absence of prior knowledge of the signal which excites the system. It is pointed out that practical blind deconvolution can only be effected iteratively at present. An iterative blind identification algorithm is described and is illustrated by recovery of images from blurred versions contaminated with noise of varying levels. Successful blind deconvolution is achieved without invoking prior knowledge of either the forms or the supports of either the original images or the point spread functions, which respectively correspond to exciting signals and response functions.     

Frequency-domain blind deconvolution based on mutual information rate

In this paper, a new blind single-input single-output (SISO) deconvolution method based on the minimization of the mutual information rate of the deconvolved output is proposed. The method works in the frequency domain and requires estimation of the signal probability density function. Thus, the algorithm uses higher order statistics (except for Gaussian source) and allows non-minimum-phase filter estimation. In practice, the criterion contains a regularization term for limiting noise amplification as in Wiener filtering. The score function estimation, which represents a key point of the algorithm, is detailed, and the most robust estimate is selected. Finally, experiments point to the relevance of the proposed algorithm: 1) any filter, minimum phase or not, can be estimated and 2) on actual data (underwater explosions, seismovolcanic phenomena), this deconvolution algorithm provides good results with a better tradeoff between deconvolution quality and noise amplification than existing methods.     

Blind MAI and ISI suppression for DS/CDMA systems using HOS-basedinverse filter criteria

Cumulant-based inverse filter criteria (IFC) using second-and higher order statistics (HOS) proposed by Tugnait et al. (1993) have been widely used for blind deconvolution of discrete-time multi-input multi-output (MIMO) linear time-invariant systems with non-Gaussian measurements through a multistage successive cancellation procedure, but the deconvolved signals turn out to be an unknown permutation of the driving inputs. A multistage blind equalization algorithm (MBEA) is proposed for multiple access interference (MAI) and intersymbol interference (ISI) suppression of multiuser direct sequence/code division multiple access (DS/CDMA) systems in the presence of multipath. The proposed MBEA, which processes the chip waveform matched filter output signal without requiring any path delay information, includes blind deconvolution processing using IFC followed by identification of the estimated symbol sequence with the associated user through a user identification algorithm (UIA). Then, some simulation results are presented to support the proposed MBEA and UIA. Finally, some conclusions are drawn     

A Balanced Approach to Multichannel Blind Deconvolution

In some general state-space approaches to the multichannel blind deconvolution problem, e.g., the information backpropagation approach (Zhang and Cichocki 2000), an implicit assumption is usually involved therein, viz., the dimension of the state vector of the mixer is known a priori. In general, if the number of states in the state space is not known a priori, Zhang and Cichocki (2000) suggested using a maximum possible number of states; this procedure will introduce additional delays in the recovered source signals. In this paper, our aim is to relax this assumption. The objective is achieved by using balanced parameterization of the underlying discrete-time dynamical system. Since there are no known balanced parameterization algorithms for discrete-time systems, we need to go through a "circuitous" route, by first transforming the discrete-time system into a continuous-time system using a bilinear transformation, perform the balanced parameterization on the resulting continuous-time system, and then transform the resulting system back to discrete-time balanced parameterized system using an inverse bilinear transformation. The number of states can be determined by the number of significant singular values in the ensuing singular value decomposition step in the balanced parameterization.     

基于高斯型点扩展函数的红外图像热源复原

为了复原红外图像的热源,采用高斯点扩展函数的方法来增强热源的清晰度和对比度。首先,确定热源图像的高斯点扩展函数,建立其退化模型;然后,采用维纳滤波的方法复原红外图像中的热源,对复原图像通过YIQ变换来复原其温度场彩色信息;最后,通过边缘锐度和标准差评价分析热源复原的质量,并与盲复原算法对比。结果表明,边缘锐度边缘锐度和标准差分别提高了0.502%和0.124%。基于高斯型点扩展函数的方法对红外图像的热源复原具有明显的效果。     

基于变分贝叶斯估计的相机抖动模糊图像的盲复原算法

在曝光过程中由于相机抖动而导致的运动模糊,是一种常见的图像降质现象。该文提出了一种基于变分贝叶斯估计和自然图像梯度统计特性的盲复原算法,用于恢复相机抖动模糊图像,同时针对图像复原过程中出现的振铃效应,设计了一种基于分区域检测和Fuzzy滤波器的去振铃效应方法。实验结果表明,该文提出的盲复原算法能够有效地去除图像中因相机抖动而产生的模糊,而且在保持图像边缘和细节的同时,可以较好地降低振铃效应对图像复原质量的影响。     

Blind identification and deconvolution of linear systems driven bybinary random sequences

The problem of blind identification and deconvolution of linear systems with independent binary inputs is addressed. To solve the problem, a linear system is applied to the observed data and adjusted so as to produce binary outputs. It is proved that the system coincides with the inverse of the unknown system (with scale and shift ambiguities), whether it is minimum or nonminimum phase. These results are derived for nonstationary independent binary inputs of infinite or finite length. Based on these results, an identification method is proposed for parametric linear systems. It is shown that under some mild conditions, a consistent estimator of the parameter can be obtained by minimizing a binariness criterion for the output data. Unlike many other blind identification and deconvolution methods, this criterion handles nonstationary signals and does not utilize any moment information of the inputs. Three numerical examples are presented to demonstrate the effectiveness of the proposed method     

Total variation blind deconvolution

We present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed by Acar and Vogel (1994). The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur. An alternating minimization (AM) implicit iterative scheme is devised to recover the image and simultaneously identify the point spread function (PSF). Numerical results indicate that the iterative scheme is quite robust, converges very fast (especially for discontinuous blur), and both the image and the PSF can be recovered under the presence of high noise level. Finally, we remark that PSFs without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.     

Texture-preserving deconvolution via image decomposition

The image with rich textures can be decomposed into the sum of a geometric part and a textural part. Inspired by this fact, we propose an efficient texture-preserving image deconvolution algorithm based on image decomposition. Our algorithm restores the geometric part and textural part, respectively, by incorporating \(L_0\) gradient minimization and a wave atoms-based Wiener shrinkage filter. The \(L_0\)-based gradient minimization method could globally locate important edges, main structures. The wave atoms transform offers a better representation of images containing oscillatory patterns and textures than other known transforms. Our method contains three steps for restoring texture images. First, we propose an image deconvolution method based on \(L_0\) gradient minimization to restore geometric part of the image with minimal loss of image detail components. Next, we use a Wiener shrinkage filter in the wave atom domain to attenuate the leaked colored noise and extract fine details. Finally, we obtain the estimated image by adding the two image parts together. We compare our deconvolution algorithm with other competitive deconvolution techniques in terms of ISNR, SSIM, and visual quality.     

Blind deconvolution of images using optimal sparse representations.

The relative Newton algorithm, previously proposed for quasi-maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used as the nonlinear term for sparse sources. In addition, we propose a method of sparsification, which allows blind deconvolution of arbitrary sources, and show how to find optimal sparsifying transformations by supervised learning.