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Astronomical image restoration using variational Bayesian blind deconvolution

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Journal of Systems Engineering and Electronics 2017年第6期28卷 1236-1247页

作者： Xiaoping Shi Rui Guo Yi Zhu Zicai Wang Control and Simulation Center Harbin Institute of Technology Harbin 150080 China

An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical *** a hierarchical Bayesian framework, the unknown original image and all required algorithmic parameters are estimated simultaneously. Through utilization of variational Bayesian analysis,approximations of the posterior distributions on each unknown are obtained by minimizing the Kullback-Leibler(KL) distance, thus providing uncertainties of the estimates during the restoration process. Experimental results on both synthetic images and real astronomical images demonstrate that the proposed approaches compare favorably to other state-of-the-art reconstruction methods.

关键词： blind deconvolution variational Bayesian model com bination astronomical image processing

PERFORMANCE OF blind deconvolution IN OPTOACOUSTIC TOMOGRAPHY

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Journal of Innovative Optical Health Sciences 2011年第4期4卷 385-393页

作者： THOMAS JETZFELLNER VASILIS NTZIACHRISTOS Institute for Biological and Medical Imaging Technische Universit€at M€unchen and Helmholtz Center Munich Ingolstaedter Landstr.1 85764 NeuherbergGermany

In this paper,we consider the use of blind deconvolution for optoacoustic(photoacoustic)imaging and investigate the performance of the method as means for increasing the resolution of the reconstructed image beyond the physical restrictions of the *** method is demonstrated with optoacoustic measurement obtained from six-day-old mice,imaged in the near-infrared using a broadband hydrophone in a circular scanning *** that estimates of the unknown point spread function,achieved by blind deconvolution,improve the resolution and contrast in the images and show promise for enhancing optoacoustic images.

关键词： Optoacoustic photoacoustic tomography multispectral blind deconvolution interpolated-model-matrix inversion(IMMI)

Multiframe blind Super Resolution Imaging Based on blind deconvolution

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Transactions of Tianjin University 2016年第4期22卷 358-366页

作者：元伟张立毅 School of Electronic Information Engineering Tianjin University College of Information Engineering Tianjin University of Commerce

As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.

关键词： blind deconvolution multiframe blind super resolution imaging regularization iteration deblurring

Lipschitz and Total-Variational Regularization for blind deconvolution

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Communications in Computational Physics 2008年第6期4卷 195-206页

作者： Yu-Mei Huang Michael K.Ng Centre for Mathematical Imaging and Vision and Department of Mathematics Hong Kong Baptist UniversityKowloon TongHong Kong. Department of Mathematics Lanzhou UniversityGansuChina.

In[3],Chan and Wong proposed to use total variational regularization for both images and point spread functions in blind *** experimental results show that the detail of the restored images cannot be *** this paper,we consider images in Lipschitz spaces,and propose to use Lipschitz regularization for images and total variational regularization for point spread functions in blind *** experimental results show that such combination of Lipschitz and total variational regularization methods can recover both images and point spread functions quite well.

关键词： Lipschitz regularization total variational regularization blind deconvolution texture Poisson singular integral alternating iterative algorithm.

Underwater image restoration by means of blind deconvolution approach

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Frontiers of Optoelectronics 2010年第2期3卷 169-178页

作者： Fan FAN Kecheng YANG Min XIA Wei LI Bo FU Wei ZHANG Wuhan National Laboratory for Optoelectronics College of Optoelectronic Science and Engineering Huazhong University of Science and Technology

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.

关键词： underwater imaging image restoration blind deconvolution modulation transfer function (MTF) point spread function (PSF)

AN IMPROVED FAST blind deconvolution ALGORITHM BASED ON DECORRELATION AND BLOCK MATRIX

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Journal of Electronics(China) 2008年第5期25卷 577-582页

作者： Yang Jun＇an He Xuefan 309 Research Division of Electronic Engineering Institute Hefei 230037 China Natural Inspired Computation and Applications Lab of USTC Hefei 230036 China

In order to alleviate the shortcomings of most blind deconvolution algorithms,this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block *** the original algorithm can overcome the shortcomings of current blind deconvolution algorithms,it has a constraint that the number of the source signals must be less than that of the *** improved algorithm deletes this constraint by using decorrelation ***,the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal *** results demonstrate the validation and fast separation of the improved algorithm.

关键词： blind deconvolution Fast algorithm Decorrelation Block matrix

Single-Image blind deconvolution Using Gradient Saliency Map

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Single-Image Blind Deconvolution Using Gradient Saliency Map

blind deconvolution Processing of Loop Inductance Signals for Vehicle Reidentification

第36届中国控制会议

作者： Xiaoguang Di Lei Yin Control and Simulation Center Harbin Institute of Technology

ISBN: (纸本)9781538629185

Single-image blind deconvolution is one of the most challenging fields in image processing which restores a sharp image from its blurred *** blind deconvolution algorithms have made significant ***,the restoration of blurred images with little scale edges and periodic textures is still a hard *** solve this problem,this paper proposes a new normalized sparse regularization blind deconvolution algorithm,which uses a gradient saliency map to prohibit the image small structures on image blurry kernel ***,salient detection is performed to select the important area which conforms with the human vision system and generates a binary mask to screen out useful ***,the normalized sparse regularization blind deconvolution method is applied to obtain accurate blur kernel and recover the sharp ***,the experiment results show that the algorithm can effectively deblur the degraded image on different scenarios.

关键词： blind deconvolution sparse regularization salient detection gradient map

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Journal of Civil Engineering and Architecture 2011年第11期5卷 957-966页

作者： Taek Mu Kwon Anushri Parsekar Department of Electrical and Computer Engineering University of Minnesota Duluth Department of Computer Science University of Minnesota Duluth

Vehicle reidentification is an elegant solution for gathering several pieces of valuable traffic information, e.g., space mean speed, travel time, vehicle tracking, and origin/destination data. Recently, a number of vehiclereidentification algorithms utilizing inductive loop signals have been proposed to take advantage of the widespread availability of loop detectors. These algorithms, however, all directly utilize the raw inductance signals for pattern matching and feature extraction without deconvolution. The raw loop signals are essentially a convolved output between the true vehicle inductance signature and the loop system function, and thus a deconvolution is needed in order to expose the detailed features of individual vehicles. The purpose of this paper is to present a recent investigation on restoration of true inductance signatures by applying a blind deconvolution process. The main advantage of blind deconvolution over the conventional deconvolution is that the computation does not require modeling of a precise loop-detector system function. Experimental results show that the proposed blind deconvolution reveals much more detailed features of inductance signals and, as a result, increases the vehicle reidentification accuracy.

关键词： Vehicle reidentification blind deconvolution loop inductance signals.

A Class of New Criteria for blind deconvolution

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A Class of New Criteria for Blind Deconvolution