Blind deconvolution and deblurring in image analysis (original) (raw)
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A nonparametric procedure for blind image deblurring
Computational Statistics & Data Analysis, 2008
Observed images are usually blurred versions of the true images, due to imperfections of the imaging devices, atmospheric turbulence, out of focus lens, motion blurs, and so forth. The major purpose of image deblurring is to restore the original image from its blurred version. A blurred image can be described by the convolution of the original image with a point spread function (psf) that characterizes the blurring mechanism. Thus, one essential problem for image deblurring is to estimate the psf from the observed but blurred image, which turns out to be a challenging task, due to the "ill-posed" nature of the problem. In the literature, most existing image deblurring procedures assume that either the psf is completely known or it has a parametric form. Motivated by some image applications, including handwritten text recognition and calibration of imaging devices, we suggest a method for estimating the psf nonparametrically, in cases when the true image has one or more line edges, which is usually satisfied in the applications mentioned above and which is not a big restriction in some other image applications, because it is often convenient to take pictures of objects with line edges, using the imaging device under study. Both theoretical justifications and numerical studies show that the proposed method works well in applications.
Parametric Blur Estimation for Blind Restoration of Natural Images: Linear Motion and Out-of-Focus
2014
This paper presents a new method to estimate the parameters of two types of blurs, linear uniform motion (approximated by a line characterized by angle and length) and out-of-focus (modeled as a uniform disk characterized by its radius), for blind restoration of natural images. The method is based on the spectrum of the blurred images and is supported on a weak assumption, which is valid for the most natural images: the power-spectrum is approximately isotropic and has a power-law decay with the spatial frequency. We introduce two modifications to the radon transform, which allow the identification of the blur spectrum pattern of the two types of blurs above mentioned. The blur parameters are identified by fitting an appropriate function that accounts separately for the natural image spectrum and the blur frequency response. The accuracy of the proposed method is validated by simulations, and the effectiveness of the proposed method is assessed by testing the algorithm on real natural blurred images and comparing it with state-of-the-art blind deconvolution methods.
Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurring
The Visual Computer, 2020
The research paper focuses on a challenging task faced in Blind Image Deblurring (BID). It relates to the estimation of arbitrarily shaped (non-parametric or complex shaped) Point Spread Functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring, in this case, requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on Genetic Algorithm (GA) and utilizes the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other state-of-the-art single image motion deblurring schemes as benchmarks. Validation has been carried out on the standard real-life blurred images. Results of both benchmark and real images are presented. For real-life blurred images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions. However, the benchmark schemes fail to effectively restore the real blurred images. The proposed scheme surpasses on average of seven percent higher image quality as compared to the benchmark schemes.
Blind iterative restoration of images with spatially - varying blur
2006
Removing non-uniform blur and noise from optical images is a very dicult,problem to resolve. In this paper we describe a strategy that can be used for solving such problems. We describe how to restore images blurred by an unknown,spatially-varying point spread function (PSF) by using a combination of methods including sectioning and phase diversity blind deconvolution. The PSFs on
Efficient Blind Image Deblurring Using Nonparametric Regression and Local Pixel Clustering
Technometrics, 2018
Blind image deblurring is a challenging ill-posed problem. It would have an infinite number of solutions even in cases when an observed image contains no noise. In reality, however, observed images almost always contain noise. The presence of noise would make the image deblurring problem even more challenging because the noise can cause numerical instability in many existing image deblurring procedures. In this paper, a novel blind image deblurring approach is proposed, which can remove both pointwise noise and spatial blur efficiently without imposing restrictive assumptions on either the point spread function (psf) or the true image. It even allows the psf to be location dependent. In the proposed approach, a local pixel clustering procedure is used to handle the challenging task of restoring complicated edge structures that are tapered by blur, and a nonparametric regression procedure is used for removing noise at the same time. Numerical examples show that our proposed method can effectively handle a wide variety of blur and it works well in applications.
A Method for Blind Deblurring of Natural Images
Image de blurring is an inverse problem whose aim to recover an image from a version of that image which has suffered a linear degradation and noise. A method for blind image de blurring is presented. The method only makes weak assumptions about the blurring filter and is able to undo wide variety of de blurring degradations .But effective ness of this approach under presence of noise is still confused. To overcome this an approach is investigated where, in presence of asymmetric noise, a preprocessing of image, i.e. denoising the image. To denoising the image we used two techniques, bilateral filtering and singular value decomposition de noising. We comparing the performance using these two methods to restore the image. To overcome the ill-posedness of the blind image de blurring problem, the method includes a learning technique which initially focuses on the main edges of the image and gradually takes details into account. A new image prior, which includes a new edge detector, is used. The method is able to handle unconstrained blurs, but also allows the use of constraints or of prior information on the blurring filter, as well as the use of filters defined in a parametric manner. Furthermore, it works in both single-frame and multiform scenarios. The use of constrained blur models appropriate to the problem at hand, and/or of multiform scenarios, generally improves the de blurring results. Tests performed on monochrome and colour images.
2016
Degradations of images during the acquisition process is inevitable; images suffer from blur and noise. With advances in technologies and computational tools, the degradations in the images can be avoided or corrected up to a significant level, however, the quality of acquired images is still not adequate for many applications. This calls for the development of more sophisticated digital image restoration tools. This thesis is a contribution to image restoration. The thesis is divided into five chapters, each including a detailed discussion on different aspects of image restoration. It starts with a generic overview of imaging systems, and points out the possible degradations occurring in images with their fundamental causes. In some cases the blur can be considered stationary throughout the field-of-view, and then it can be simply modeled as convolution. However, in many practical cases, the blur varies throughout the field-of-view, and thus modeling the blur is not simple consider...
An Efficient Blind Image Deblurring Using a Smoothing Function
Applied Computational Intelligence and Soft Computing
This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images con...
Blind image deblurring using jump regression analysis
Statistica Sinica
Observed images are often blurred. Blind image deblurring (BID) is for estimating a true image from its observed but blurred version, when the blurring mechanism described by a point spread function (psf) cannot be completely specified beforehand. This is a challenging "ill-posed" problem, because (i) theoretically speaking, the true image cannot be uniquely determined by the observed image when the psf is unknown, and (ii) practically, besides blur, observed images often contain noise that would bring numerical instability to the image deblurring problem. In the literature, early image deblurring methods are developed under the assumption that the psf is known. More recent methods try to avoid this restrictive assumption, by assuming that either the psf follows a parametric form with some unknown parameters, or the true image has certain special structures. In this paper, we propose a BID methodology, without imposing restrictive assumptions on the psf or the true image. It even allows the psf to change over location. Our method makes use of the hierarchical nature of blurring that the image structure is altered most significantly around step edges, less significantly around roof/valley edges, and least significantly at places where the true image intensity function is straight. So, it pays special attention to regions around step and roof/valley edges when deblurring. Theoretical justifications and numerical studies show that it works well in applications.