检索结果-南通市图书馆

Image defocus deblurring method based on gradient difference of boundary neighborhood

Virtual Reality & Intelligent Hardware 2023年第6期5卷 538-549页

作者： Junjie TAO Yinghui WANG Haomiao MA Tao YAN Lingyu AI Shaojie ZHANG Wei LI School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China School of Computer Science Shaanxi Normal UniversityXi’an 710119China School of Internet of Things Engineering Jiangnan UniversityWuxi 214122China

Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amount,and prior knowledge in nonblind deconvolution is not strong,which leads to image detail recovery challenges.Methods To this end,this study proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood,which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring,thereby preventing boundary ringing artifacts.The obtained blur map is then used for blur detection to determine whether the image needs to be deblurred,thereby improving the efficiency of deblurring without manual intervention and judgment.Finally,a nonblind deconvolution algorithm was designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.Results Experimental results showed that our method improves PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)by an average of 4.6%and 7.3%,respectively,compared to existing methods.Conclusions Experimental results showed that the proposed method outperforms existing methods.Compared to existing methods,our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

关键词： Defocused image deblurring Gradient Boundary neighborhood Blur amount estimation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Image Blind deblurring Using an Adaptive Patch Prior

引用

Tsinghua Science and Technology 2019年第2期24卷 238-248页

作者： Yongde Guo Hongbing Ma Department of Electronic Engineering Tsinghua University Department of Electronic Engineering Tsinghua University College of Information Science and Engineering Xinjiang University

Image blind deblurring uses an estimated blur kernel to obtain an optimal restored original image with sharp features from a degraded image with blur and noise artifacts. This method, however, functions on the premise that the kernel is estimated accurately. In this work, we propose an adaptive patch prior for improving the accuracy of kernel estimation. Our proposed prior is based on local patch statistics and can rebuild low-level features,such as edges, corners, and junctions, to guide edge and texture sharpening for blur estimation. Our prior is a nonparametric model, and its adaptive computation relies on internal patch information. Moreover, heuristic filters and external image knowledge are not used in our prior. Our method for the reconstruction of salient step edges in a blurry patch can reduce noise and over-sharpening artifacts. Experiments on two popular datasets and natural images demonstrate that the kernel estimation performance of our method is superior to that of other state-of-the-art methods.

关键词： blind deblurring adaptive patch prior kernel estimation low-level features internal patch information

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Blind Image deblurring via Adaptive Optimization with Flexible Sparse St rue ture Control

引用

Journal of Computer Science & Technology 2019年第3期34卷 609-621页

作者： Ri-Sheng Liu Cai-Sheng Mao Zhi-Hui Wang Hao-Jie Li International School of Information Science and Engineering Dalian University of TechnologyDalian 116620China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian 116620China

Blind image deblurring is a long-standing ill-posed inverse problem which aims to recover a latent sharp image given only a blurry observation.So far,existing studies have designed many effective priors w.***.t.the latent image within the maximum a posteriori(MAP)framework in order to narrow down the solution space.These non-convex priors are always integrated into the final deblurring model,which makes the optimization challenging.However,due to unknown image distribution,complex kernel structure and non-uniform noises in real-world scenarios,it is indeed challenging to explicitly design a fixed prior for all cases.Thus we adopt the idea of adaptive optimization and propose the sparse structure control(SSC)for the latent image during the optimization process.In this paper,we only formulate the necessary optiinization constraints in a lightweight MAP model with no priors.Then we develop an inexact projected gradient scheme to incorporate flexible SSC in MAP inference.Besides Zp-norm based SSC in our previous work,we also train a group of denoising convolutional neural networks(CNNs)to learn the sparse image structure automatically from the training data under different noise levels,and we show that CNNs-based SSC can achieve similar results compared with Zp-norm but are more robust to noise.Extensive experiments demonstrate that the proposed adaptive optimization scheme with two types of SSC achieves the state-of-the-art results on both synthetic data and real-world images.

关键词： blind image deblurring conv olutio nal neural net work(CNN) non-convex optimization sparse structure control(SSC)

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Defocus Hyperspectral Image deblurring with Adaptive Reference Image and Scale Map

引用

Journal of Computer Science & Technology 2019年第3期34卷 569-580页

作者： De-Wang Li Lin-Jing Lai Hua Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China

Defocus blur is one of the primary problems among hyperspectral imaging systems equipped with simple lenses. Most of the previous deblurring methods focus on how to utilize structure information of a single channel, while ignoring the characteristics of hyperspectral images. In this work, we analyze the correlations and differences among spectral channels, and propose a deblurring framework for defocus hyperspectral images. First, we divide the hyperspectral image channels into two sets, and the set with less blur is treated as a group of spectral bases. Then, according to the inherent correlations of spectral channels, a reference image can be derived from the spectral bases to guide the restoration of blurry channels. Finally, considering the disagreement between the reference image and the ground truth, a scale map based on gradient similarity is introduced as a prior in the deblurring framework. The experimental results on public dataset demonstrate that the proposed method outperforms several image deblurring methods in both visual effect and quality metrics.

关键词： hyperspectral image defocus blur deblurring reference image scale map

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Image Motion deblurring Based on Salient Structure Selection and L0-2 Norm Kernel Estimation

引用

Journal of Computer and Communications 2017年第3期5卷 24-32页

作者： Fuwei Zhang Yumin Tian Xidian University Xi’an China

Single image motion deblurring has been a very challenging problem in the field of image processing. Although there are many researches had been proposed to solve this problem, it still has problems on kernel accuracy. In order to improve the kernel accuracy, an effective structure selection method was used to select the salient structure of the blur image. Then a novel kernel estimation method based on L0-2 norm was proposed. To guarantee the sparse kernel and eliminate the negative influence of details L0-norm was used. And L2-norm was used to ensure the continuity of kernel. Many experiments were done to compare proposed method and state-of-the-art methods. The results show that our method can estimate a better kernel and use less time than previous work, especially when the size of blur kernel is large.

关键词： Motion deblurring Structure Selection Kernel Estimation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

deblurring,artifact-free optical coherence tomography with deconvolution-random phase modulation

引用

Opto-Electronic Science 2024年第1期3卷 13-24页

作者： Xin Ge Si Chen Kan Lin Guangming Ni En Bo Lulu Wang Linbo Liu School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Chemical and Biomedical Engineering Nanyang Technological UniversitySingapore 637459Singapore China-Singapore International Joint Research Institute(CSIJRI) Guangzhou 510000China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054China

Deconvolution is a commonly employed technique for enhancing image quality in optical imaging methods.Unfortu-nately,its application in optical coherence tomography(OCT)is often hindered by sensitivity to noise,which leads to ad-ditive ringing artifacts.These artifacts considerably degrade the quality of deconvolved images,thereby limiting its effect-iveness in OCT imaging.In this study,we propose a framework that integrates numerical random phase masks into the deconvolution process,effectively eliminating these artifacts and enhancing image clarity.The optimized joint operation of an iterative Richardson-Lucy deconvolution and numerical synthesis of random phase masks(RPM),termed as De-conv-RPM,enables a 2.5-fold reduction in full width at half-maximum(FWHM).We demonstrate that the Deconv-RPM method significantly enhances image clarity,allowing for the discernment of previously unresolved cellular-level details in nonkeratinized epithelial cells ex vivo and moving blood cells in vivo.

关键词： deconvolution random phase masks deblurring

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Blind Motion deblurring for Online Defect Visual Inspection

引用

国际计算机前沿大会会议论文集 2019年第2期 86-89页

作者： Guixiong Liu Bodi Wang Junfang Wu School of Mechanical and Automotive Engineering South China University of Technology Guangzhou 510640 China School of Physics South China University of Technology Guangzhou 510640 China

Online defect visual inspection (ODVI) works while the object has to be static, otherwise the relative motion between camera and object will create motion blur in images. In order to implement ODVI in dynamic scene, it developes one blind motion deblurring method whose objective is to estimate blur kernel parameters precisely. In the proposed method, Radon transform on superpixels determinated the blur angle, and the autocorrelation function based on magnitude (AFM) of the preprocessed blurred image was utilized to identify the blur length. With the projection relationship discussed in this study, it will be unnecessary to rotate the blurred image or the axis. The proposed method is of high accuracy and robustness to noise, and it can somehow handle saturated pixels. To validate the proposed method, experiments have been carried out on synthetic images both in noise free and noisy situations. The results show that the method outperforms existing approaches. With the modified Richardson– Lucy deconvolution, it demonstrates that the proposed method is effective for ODVI in terms of subjective visual quality.

关键词： Blind motion deblurring Blur kernel estimation Radon transform Autocorrelation function Saturated pixels

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

The Convex Relaxation Method on Deconvolution Model with Multiplicative Noise

引用

Communications in Computational Physics 2013年第4期13卷 1066-1092页

作者： Yumei Huang Michael Ng Tieyong Zeng School of Mathematics and Statistics Lanzhou UniversityLanzhou 730000China Department of Mathematics Hong Kong Baptist UniversityKowloon TongHong Kong

In this paper,we consider variational approaches to handle the multiplicative noise removal and deblurring problem.Based on rather reasonable physical blurring-noisy assumptions,we derive a new variational model for this issue.After the study of the basic properties,we propose to approximate it by a convex relaxation model which is a balance between the previous non-convex model and a convex model.The relaxed model is solved by an alternating minimization approach.Numerical examples are presented to illustrate the effectiveness and efficiency of the proposed method.

关键词： Alternating minimization convergence deblurring multiplicative noise non-convex model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Radon transform-based motion blurred silkworm pupa image restoration

引用

International Journal of Agricultural and Biological Engineering 2019年第2期12卷 152-159页

作者： Dan Tao Zhengrong Wang Guanglin Li Guangying Qiu College of Engineering and Technology Southwest UniversityChongqing 400715China

As for machine vision-based intelligent system in the application of discriminating and sorting the sex of silkworm pupae,the tail gonad was the unique physiological feature.However,motion blur,resulting from the live silkworm pupa’s writhing motion at the moment of capturing image,could lose textures and structures(such as edge and tail gonad etc.)dramatically,which casted great challenges for sex identification.To increase the image quality and relieve the difficulty of discrimination caused by motion blur,an effective approach that including three stages was proposed in this work.In the image prediction stage,first sharp edges were acquired by using filtering techniques.Then the initial blur kernel was computed with Gaussian prior.The coarse version latent image was deconvoluted in the Fourier domain.In the kernel refinement stage,the Radon transform was applied to estimate the accurate kernel.In the final restoration step,a TV-L1 deconvolution model was carried out to obtain a better result.The experimental results showed that benefiting from the prediction step and kernel refinement step,the kernel was more accurate and the recovered image contained much more textures.It revealed that the proposed method was useful in removing the motion blur.Furthermore,the method could also be applied to other fields.

关键词： silkworm pupa image restoration radon transform machine vision motion blur deblurring

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Adaptive Strategy for the Restoration of Textured Images using Fractional Order Regularization

引用

Numerical Mathematics(Theory,Methods and Applications) 2013年第1期6卷 276-296页

作者： R.H.Chan A.Lanza S.Morigi F.Sgallari Department of Mathematics The Chinese University of Hong KongShatinHong Kong Department of Mathematics–CIRAM University of BolognaVia Saragozza8BolognaItaly Department of Mathematics University of BolognaPiazza Porta San Donato5BolognaItaly

Total variation regularization has good performance in noise removal and edge preservation but lacks in texture restoration.Here we present a texture-preserving strategy to restore images contaminated by blur and noise.According to a texture detection strategy,we apply spatially adaptive fractional order diffusion.A fast algorithm based on the half-quadratic technique is used to minimize the resulting objective function.Numerical results show the effectiveness of our strategy.

关键词： Ill-posed problem deblurring fractional order derivatives regularizing iterative method

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：