Blind motion deblurring using optical flow

Xu, Yuquan; Hu, Xiyuan; Peng, Silong

doi:10.1016/j.ijleo.2014.08.139

Cited by 18 publications

(15 citation statements)

References 38 publications

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“…[20] were compared with the proposed method. Xu et al [3] proposed the nonuniform point spread function on the basis of the optical flow estimation model for removing blurring caused by camera shakes. Whyte et al [20] proposed a parametrized geometric model for camera shakes and applied it for deblurring within the framework of existing camera shake removal algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…(1) Input the 100 blurry images from the image queue. (2) Perform preprocessing, image deblurring (using the model [3,20]), and ARG segmentation. (3) Perform DWT-based extraction.…”

Section: Resultsmentioning

confidence: 99%

“…Several deblurring methods have been proposed to address object motion [1] and camera shakes [2]. Xu et al [3] proposed an optical flow-based model to simultaneously correct both types of blurs. However, a basic assumption is required in the optical flow algorithm: the pixel intensity or color does not change when the pixel flows from one image to another.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Edge Feature Description Method for Blur Detection in Manufacturing Processes

Lin

2016

Journal of Sensors

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A novel inspection sensor by using an edge feature description (EFD) algorithm based on a support vector machine (SVM) is proposed for industrial inspection of images. This method detects and adaptively segments blurred images by using the proposed algorithm, which uses EFD to effectively classify blurred samples and improve the conventional methods of inspecting blurred objects; the algorithm selects and optimally tunes suitable features. The proposed sensor applies a suitable feature-extraction strategy on the basis of the sensing results. Experimental results demonstrate that the proposed method outperforms the existing methods.

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Section: Resultsmentioning

confidence: 99%

“…(1) Input the 100 blurry images from the image queue. (2) Perform preprocessing, image deblurring (using the model [3,20]), and ARG segmentation. (3) Perform DWT-based extraction.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Edge Feature Description Method for Blur Detection in Manufacturing Processes

Lin

2016

Journal of Sensors

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“…Another approach to SV BD is to parameterize kernels with a small number of parameters, assuming specific blur types, e.g., motion blur [6], [11], [19]- [23] or defocus blur [15], [24]- [27]. A drawback of such methods is that they can only handle targeted blur types.…”

Section: Shift-invariant and Variant Blind Deconvolutionmentioning

confidence: 99%

Shift-Variant Blind Deconvolution Using a Field of Kernels

Sonogashira

Iiyama

Minoh

2017

IEICE Trans. Inf. & Syst.

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Motoharu SONOGASHIRA†a) , Nonmember, Masaaki IIYAMA †b) , Member, and Michihiko MINOH †c) , Fellow SUMMARY Blind deconvolution (BD) is the problem of restoring sharp images from blurry images when convolution kernels are unknown. While it has a wide range of applications and has been extensively studied, traditional shift-invariant (SI) BD focuses on uniform blur caused by kernels that do not spatially vary. However, real blur caused by factors such as motion and defocus is often nonuniform and thus beyond the ability of SI BD. Although specialized methods exist for nonuniform blur, they can only handle specific blur types. Consequently, the applicability of BD for general blur remains limited. This paper proposes a shift-variant (SV) BD method that models nonuniform blur using a field of kernels that assigns a local kernel to each pixel, thereby allowing pixelwise variation. This concept is realized as a Bayesian model that involves SV convolution with the field of kernels and smoothing of the field for regularization. A variationalBayesian inference algorithm is derived to jointly estimate a sharp latent image and a field of kernels from a blurry observed image. Owing to the flexibility of the field-of-kernels model, the proposed method can deal with a wider range of blur than previous approaches. Experiments using images with nonuniform blur demonstrate the effectiveness of the proposed SV BD method in comparison with previous SI and SV approaches. key words: blind deconvolution, deblurring, shift-variant, variational Bayes IntroductionBlind deconvolution (BD) for deblurring is one of the most extensively studied topics in image processing [1], [2]. Essentially, blurring of images is modeled as convolution of images and kernels. Deconvolution is the inverse of this process, which effectively restores sharp images from blurry ones. The objective of BD is to perform deconvolution when kernels are unknown, which is often the case in practice [1]. Since undesirable blur is quite common in the real world, BD has a wide range of applications, e.g., mobile photography [3], computational photography [4], computer vision [5], astronomical imaging [6], and biomedical imaging [7].Traditional shift-invariant (SI) BD assumes uniform blur produced by kernels that do not vary spatially across images, such as the example in Fig. 1 (b). However, in reality blur kernels often spatially vary, producing nonuni- . For example, independently moving objects produce different effects of motion blur, and objects distant from a camera are more subject to defocus blur than close objects. Blur is often more complex since both motion and defocus blur can simultaneously occur, as shown in Fig. 1 (c). In such cases, SI BD methods fail in kernel estimation and also in deconvolution [1]. Although specialized BD methods for nonuniform blur have been recently developed, they can only handle certain types of blur, e.g., motion, defocus, or locally uniform blur [2]. Consequently, the applicability of BD to general blurry images remains limited.I...

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“…In digital image processing, blurring is a common but unwanted artifact that hides vital information that exists in an image (Cui et al, 2014). Furthermore, it is one of the main factors that lead to poor image quality (Xu et al, 2015). In many cases, it is hard to avoid the blurring artifact as it frequently ruins an image (Cho et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A novel Zohair filter for deblurring computed tomography medical images

Al-Ameen

Sulong

2015

Int. J. Imaging Syst. Technol.

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Deblurring computed tomography (CT) images has been an active research topic in recent years because of the wide variety of challenges it offers. Hence, a novel filter is proposed in this article unveiling a simple, efficient, and fast deblurring process that involves few parameters, low calculations and does not utilize the undesirable iterative property or introduce the common deblurring problems. The newly proposed filter is validated on both real and synthetic blurred CT images to provide a sufficient understanding about its performance. Moreover, proper comparisons are made with high-profile deblurring methods, in which the results are evaluated using three reliable quality metrics of feature similarity index (FSIM), structural similarity (SSIM), and visual information fidelity in pixel domain (VIFP). The intensive experiments and performance evaluations exhibited the efficiency of the proposed filter, in that it outperformed all the comparative methods.

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Blind motion deblurring using optical flow

Cited by 18 publications

References 38 publications

A Novel Edge Feature Description Method for Blur Detection in Manufacturing Processes

A Novel Edge Feature Description Method for Blur Detection in Manufacturing Processes

Shift-Variant Blind Deconvolution Using a Field of Kernels

A novel Zohair filter for deblurring computed tomography medical images

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