检索结果-内蒙古大学图书馆

blind image deconvolution through Bezoutians

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2017年 315卷 98-106页

作者： Diaz-Toca, Gema M. Belhaj, Skander Univ Murcia Dept Ingn & Tecnol Comp Murcia Spain Manouba Univ ISAMM Manouba Tunisia Univ Tunis El Manar ENIT LAMSIN Tunis Tunisia

In this paper, we introduce a fast algorithm for computing the univariate GCD of several polynomials (not pairwise) based on the generalized Bezout matrix by using Barnett's method. This novel approach is devoted to presenting an algorithm that permits to solve the problem of blind image deconvolution by computing greatest common divisors (GCD) of several polynomials. Specifically, we design a specialized algorithm for computing the GCD of bivariate polynomials of blurred images which correspond to z-transforms to recover the original image. All algorithms have been implemented in Matlab and experimental results with synthetically blurred images are included to illustrate the effectiveness of our approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Bezout matrix Barnett's method Greatest common divisors FFT blind image deconvolution

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution using a robust GCD approach

引用

IEEE TRANSACTIONS ON image PROCESSING 1999年第2期8卷 295-301页

作者： Pillai, SU Liang, B Polytech Univ Dept Elect Engn Brooklyn NY 11201 USA

In this correspondence, a new viewpoint is proposed for estimating an image from its distorted versions in presence of noise without the a priori knowledge of the distortion functions. In z-domain, the desired image can be regarded as the greatest common polynomial divisor among the distorted versions. With the assumption that the distortion filters are finite impulse response (FIR) and relatively coprime, in the absence of noise, this becomes a problem of taking the greatest common divisor (GCD) of two or more two-dimensional (2-D) polynomials. Exact GCD is not desirable because even extremely small variations due to quantization error or additive noise can destroy the integrity of the polynomial system and lead to a trivial solution. Our approach to this blind deconvolution approximation problem introduces a new robust interpolative 2-D GCD method based on a one-dimensional (1-D) Sylvester-type GCD algorithm. Experimental results with both synthetically blurred images and real motion-blurred pictures show that it is computationally efficient and moderately noise robust.

关键词： blind image deconvolution equalization image processing

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution via Hankel based method for computing the GCD of polynomials

引用

MATHEMATICS AND COMPUTERS IN SIMULATION 2018年 144卷 138-152页

作者： Belhaj, Skander Ben Kahla, Haithem Dridi, Marwa Moakher, Maher Manouba Univ ISAMM Campus Univ Manouba Tunis 2010 Tunisia Univ Tunis El Manar ENIT LAMSIN BP 37 Tunis 1002 Tunisia

In this paper we present an algorithm, that is based on computing approximate greatest common divisors (GCD) of polynomials, for solving the problem of blind image deconvolution. Specifically, we design a specialized algorithm for computing the GCD of bivariate polynomials corresponding to z-transforms of blurred images to recover the original image. The new algorithm is based on the fast GCD algorithm for univariate polynomials in which the successive transformation matrices are upper triangular Toeplitz matrices. The complexity of our algorithm is O(n(2) log(n)) where the size of blurred images is n x n. All algorithms have been implemented in Matlab and experimental results with synthetically blurred images are included to illustrate the effectiveness of our approach. (C) 2017 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Approximate GCD Hankel matrix Triangular Toeplitz inversion Fast Fourier transform blind image deconvolution

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution for single motion-blurred image

引用

The Journal of China Universities of Posts and Telecommunications 2010年第3期17卷 104-109页

作者： SUN Shao-jie WU Qiong Li Guo-hui College of Information System and Management National University of Defense Technology Changsha 410073 China Airforce Training Equipment Institute Beijing 100195 China

Motion blur due to camera shake during exposure is one of the most common reasons of image degradation, which usually reduces the quality of photographs seriously. Based on the statistical properties of the natural image＇s gradient and the blur kernel, a blind deconvolution algorithm is proposed to restore the motion-blurred image caused by camera shake, adopting the variational Bayesian estimation theory. In addition, the ring effect is one problem that is not avoided in the process of image deconvolution, and usually makes the visual effect of the restored image badly. So a dering method is put forward based on the sub-region detection and fuzzy filter. Tested on the real blurred photographs, the experimental results show that the proposed algorithm of blind image deconvolution can remove the camera-shake motion blur from the degraded image effectively, and can eliminate the ring effect better, while preserve the edges and details of the image well.

关键词： blind image deconvolution motion blur camera shake ring effect

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution via dispersion minimization

引用

DIGITAL SIGNAL PROCESSING 2006年第2期16卷 137-148页

作者： Vural, C Sethares, WA Sakarya Univ TR-54187 Sakarya Turkey Univ Wisconsin Elect & Comp Engn Dept Madison WI 53706 USA

In linear image restoration, the point spread function of the degrading system is assumed known even though this information is usually not available in real applications. As a result, both blur identification and image restoration must be performed from the observed noisy blurred image. This paper presents a computationally simple iterative blind image deconvolution method which is based on non-linear adaptive filtering. The new method is applicable to minimum as well as mixed phase blurs. The noisy blurred image is assumed to be the output of a two-dimensional linear shift-invariant system with an unknown point spread function contaminated by an additive noise. The method passes the noisy blurred image through a two-dimensional finite impulse response adaptive filter whose parameters are updated by minimizing the dispersion. When convergence occurs, the adaptive filter provides an approximate inverse of the point spread function. Moreover, its output is an estimate of the unobserved true image. Experimental results are provided. (c) 2005 Elsevier Inc. All rights reserved.

关键词： blur identification blind image deconvolution image restoration non-linear adaptive filtering constant modulus algorithm image and multimedia processing general methods

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution using the Fields of Experts prior

引用

OPTICS COMMUNICATIONS 2012年第24期285卷 5051-5061页

作者： Dong, Wende Feng, Huajun Xu, Zhihai Li, Qi Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou 310027 Zhejiang Peoples R China

In this paper, we present a method for single image blind deconvolution. To improve its ill-posedness, we formulate the problem under Bayesian probabilistic framework and use a prior named Fields of Experts (FOE) which is learnt from natural images to regularize the latent image. Furthermore, due to the sparse distribution of the point spread function (PSF), we adopt a Student-t prior to regularize it. An improved alternating minimization (AM) approach is proposed to solve the resulted optimization problem. Experiments on both synthetic and real world blurred images show that the proposed method can achieve results of high quality. (C) 2012 Elsevier B.V. All rights reserved.

关键词： blind image deconvolution Fields of Experts (FOE) prior Student-t prior Alternating minimization (AM) approach

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution using a banded matrix method

引用

NUMERICAL ALGORITHMS 2013年第1期64卷 43-72页

作者： Danelakis, Antonios Mitrouli, Marilena Triantafyllou, Dimitrios Univ Athens Dept Informat & Telecommun Athens 15784 Greece Univ Athens Dept Math Athens 15784 Greece Hellen Army Acad Dept Mil Sci Sect Math & Engn Sci Vari Greece

In this paper we study the blind image deconvolution problem in the presence of noise and measurement errors. We use a stable banded matrix based approach in order to robustly compute the greatest common divisor of two univariate polynomials and we introduce the notion of approximate greatest common divisor to encapsulate the above approach, for blind image restoration. Our method is analyzed concerning its stability and complexity resulting to useful conclusions. It is proved that our approach has better complexity than the other known greatest common divisor based blind image deconvolution techniques. Examples illustrating our procedures are given.

关键词： blind image deconvolution Blurring function Univariate polynomial Greatest Common Divisor Banded matrix Convolution

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution Using the Gaussian Scale Mixture Fields of Experts Prior 5

Blind Image Deconvolution Using the Gaussian Scale Mixture F...

引用

2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Shuyin Tao Wende Dong Zhenmin Tang Qiong Wang School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Collaborative Innovation Center of Social Safety Science and Technology Nanjing University of Science and Technology The 28th Research Institute of China Electronic Technology Group Corporation

ISBN: (纸本)9781538619797;9781538619780

In this paper, a blind image deconvolution method which is derived from Bayesian probabilistic framework is proposed. A robust prior named Gaussian Scale Mixture Fields of Experts（GSM FoE） and a prior that is constructed with the l-norm（ p≈1.5） are adopted to regularize the latent image and the point spread function（PSF） respectively. We use a two phase optimization approach to solve the resulted maximum a-posteriori（MAP） estimation problem, and a simple gradient selecting method is incorporated into the alternating minimization to improve the accuracy of the estimated PSF. Experiments on both synthetic and real world blurred images show that our method can achieve results with high quality.

关键词： blind image deconvolution GSM FoE lp-norm regularization MAP

来源：评论

学校读者我要写书评

暂无评论

blind image deconvolution USING THE SYLVESTER RESULTANT MATRIX

BLIND IMAGE DECONVOLUTION USING THE SYLVESTER RESULTANT MATR...

引用

IEEE International Conference on image Processing (ICIP)

作者： Alkhaldi, Nora Winkler, Joab Univ Sheffield Dept Comp Sci Sheffield S10 2TN S Yorkshire England King Faisal Univ Dept Comp Sci Al Hasa Saudi Arabia

ISBN: (纸本)9781479983391

This paper uses techniques from computational algebraic geometry to perform blind image deconvolution, such that prior knowledge of the point spread function (PSF) is not required to compute a deblurred form of a given blurred image. In particular, it is shown that the Sylvester resultant matrix enables the PSF to be calculated by two approximate greatest common divisor computations. These computations, and not greatest common divisor computations, are required because of the noise that is present in the exact image and PSF. The computed PSF is then deconvolved from the blurred image in order to calculate the deblurred image. The experimental results show consistently good results for the deblurred image and PSF, and they are compared with the results from other methods for blind image deconvolution.

关键词： image restoration blind image deconvolution Sylvester matrix greatest common divisor

来源：评论

学校读者我要写书评

暂无评论

A soft double regularization approach to parametric blind image deconvolution

引用

IEEE TRANSACTIONS ON image PROCESSING 2005年第5期14卷 624-633页

作者： Chen, L Yap, KH Nanyang Technol Univ Sch Elect & Elect Engn Media Technol Lab Singapore 639798 Singapore

This paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures. Conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information. This paper attempts to address this issue by assessing the relevance of parametric blur information, and incorporating the knowledge into the parametric double regularization (PDR) scheme. The PDR method assumes that the actual blur satisfies up to a certain degree of parametric structure, as there are many well-known parametric blurs in practical applications. Further, it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available. A manifold soft parametric modeling technique is proposed to generate the blur manifolds, and estimate the fuzzy blur structure. The PDR scheme involves the development of the meaningful cost function, the estimation of blur support and structure, and the optimization of the cost function. Experimental results show that it is effective in restoring degraded images under different environments.

关键词： blind image deconvolution blur identification conjugate gradient optimization double regularization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：