Image Deconvolution Method Research Articles

A New Approach of Blind Image Deconvolution Method without Artifact along the Blurring Paths

A New Approach of Blind Image Deconvolution Method without Artifact along the Blurring Paths

A Wavelet-Laplace Variational Technique for Image Deconvolution and Inpainting

We construct a new variational method for blind deconvolution of images and inpainting, motivated by recent PDE-based techniques involving the Ginzburg-Landau functional, but using more localized wavelet-based methods. We present results for both binary and grayscale images. Comparable speeds are achieved with better sharpness of edges in the reconstruction.

Robust defocus blur identification in the context of blind image quality assessment

A defocus blur metric for use in blind image quality assessment is proposed. Blind image deconvolution methods are used to determine the metric. Existing direct deconvolution methods based on the cepstrum, bicepstrum and on a spectral subtraction technique are compared across 210 images. A variation of the spectral subtraction method, based on a power spectrum surface of revolution, is proposed and is found to compare favourably with existing direct deconvolution methods for defocus blur identification. The method is found to be especially useful when distinguishing between in-focus and out-of-focus images.

TU-FF-A4-06: Motion Blurring Correction for a Cone Beam CT System with Image Decomposition and Deconvolution Method

Purpose: To improve the image resolution of a cone beam breast CT system by correcting the image blurring caused by gantry motion for a system works under continuous fluoroscopy acquisition mode. Method and Materials: Computer simulation models were built to simulate the effects on CT system resolution from different subcomponents, including the focal spot distribution and gantry motion. The system MTF results showed that image resolution degraded from the center towards the edge of the FOV, along the azimuthal direction. The major cause of this degradation was the gantry motion during continuous fluoroscopy acquisition. Azimuthal MTF results from computer simulation were used to generate a 2-D MTF response function to model the system resolution property by fitting discrete data points into a set of 3rd order polynomials. To preserve the image resolution of CT images on the radial direction, the original CT images were converted from the Cartesian coordinates into a new orthogonal coordinate system by the following transformation: For each radial position, the image data were de-convolved with corresponding system MTF in frequency domain along the azimuthal direction (along T axis). CT Images were converted back to Cartesian coordinates after deconvolution by: Results: A full view 2D MTF function was generated from computer simulations. With the presented method, the azimuthal resolution of the CT image was improved while the radial resolution was preserved. This method was applied to simulated phantom images and clinical breast CT images, qualitative observations showed improved spatial resolution along azimuthal direction. Conclusion: The full view 2D MTF function proved to be sufficient to model the spatial resolution of a cone beam CT scanner, which is a non-isotropic, shift-variant system. The presented novel image decomposition and deconvolution method demonstrated the promising potential to correct for the spatial resolution degradation.

3D phase diversity: a myopic deconvolution method for short-exposure images: application to retinal imaging

3D deconvolution is an established technique in microscopy that may be useful for low-cost high-resolution imaging of the retina. We report on a myopic 3D deconvolution method developed in a Bayesian framework. This method uses a 3D imaging model, a noise model that accounts for both photon and detector noises, a regularization term that is appropriate for objects that are a mix of sharp edges and smooth areas, a positivity constraint, and a smart parameterization of the point-spread function (PSF) by the pupil phase. It estimates the object and the PSF jointly. The PSF parameterization through the pupil phase constrains the inversion by dramatically reducing the number of unknowns. The joint deconvolution is further constrained by an additional longitudinal support constraint derived from a 3D interpretation of the phase-diversity technique. This method is validated by simulated retinal images.

Image Deblurring in the Presence of Impulsive Noise

Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The ...

High-resolution ultrasonic imaging using two-dimensional homomorphic filtering

A new method for two-dimensional deconvolution of medical ultrasonic images is presented. The spatial resolution of the deconvolved images is much higher compared to the common images of the fundamental and second harmonic. The deconvolution also results in a more distinct speckle pattern. Unlike the most published deconvolution algorithms for ultrasonic images, the presented technique can be implemented using currently available hardware in real-time imaging, with a rate up to 50 frames per second. This makes it attractive for application in the current ultrasound scanners. The algorithm is based on two-dimensional homomorphic deconvolution with simplified assumptions about the point spread function. Broadband radio frequency image data are deconvolved instead of common fundamental harmonic data. Thus, information of both the first and second harmonics is used. The method was validated on image data recorded from a tissue-mimicking phantom and on clinical image data.

Blind image deconvolution using the zero-lag slice of higher-order statistics

A novel algorithm for blind image deconvolution using the zero-lag slice (ZLS) of higher-order statistics only is presented. This method first estimates the point-spread function (PSF) using the ZLS of its third-order moment (TOM) and then uses it with one of the known classical image deconvolution methods. The proposed method has simple computations for PSF estimation because it solves a nonlinear problem by using an iterative method with fast convergence. In each iteration, one need only calculate the ZLS of the TOM and estimate the PSF using simple two-dimensional operations. Furthermore, the method presented achieves good results, since the ZLS estimate obtained from the degraded image exhibits high reliability. The good performance of the proposed algorithm is demonstrated by applying it to synthetic and real data sets.

Single frame blind image deconvolution by non-negative sparse matrix factorization

Novel approach to single frame multichannel blind image deconvolution has been formulated recently as non-negative matrix factorization problem with sparseness constraints imposed on the unknown mixing vector that accounts for the case of non-sparse source image. Unlike most of the blind image deconvolution algorithms, the novel approach assumed no a priori knowledge about the blurring kernel and original image. Our contributions in this paper are: (i) we have formulated generalized non-negative matrix factorization approach to blind image deconvolution with sparseness constraints imposed on either unknown mixing vector or unknown source image; (ii) the criteria are established to distinguish whether unknown source image was sparse or not as well as to estimate appropriate sparseness constraint from degraded image itself, thus making the proposed approach completely unsupervised; (iii) an extensive experimental performance evaluation of the non-negative matrix factorization algorithm is presented on the images degraded by the blur caused by the photon sieve, out-of-focus blur with sparse and non-sparse images and blur caused by atmospheric turbulence. The algorithm is compared with the state-of-the-art single frame blind image deconvolution algorithms such as blind Richardson–Lucy algorithm and single frame multichannel independent component analysis based algorithm and non-blind image restoration algorithms such as multiplicative algebraic restoration technique and Van-Cittert algorithms. It has been experimentally demonstrated that proposed algorithm outperforms mentioned non-blind and blind image deconvolution methods.

Application of iterative blind deconvolution to the reconstruction of LBT LINC-NIRVANA images

Context.The paper is about methods for multiple image deconvolution and their application to the reconstruction of the images acquired by the Fizeau interferometer, denoted LINC-NIRVANA, under development for the Large Binocular Telescope (LBT). The multiple images of the same target are obtained with different orientations of the baseline.

Blind image deconvolution via dispersion minimization

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.

A soft double regularization approach to parametric blind image deconvolution

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.

Anisoplanatic deconvolution of adaptive optics images

A modified method for maximum-likelihood deconvolution of astronomical adaptive optics images is presented. By parametrizing the anisoplanatic character of the point-spread function (PSF), a simultaneous optimization of the spatially variant PSF and the deconvolved image can be performed. In the ideal case of perfect information, it is shown that the algorithm is able to perfectly cancel the adverse effects of anisoplanatism down to the level of numerical precision. Exploring two different modes of deconvolution (using object bases of pixel values or stellar field parameters), we then quantify the performance of the algorithm in the presence of Poissonian noise for crowded and noncrowded stellar fields.

Blind deconvolution constrained by Knox–Thompson spectral phases for photon-limited stellar images

A blind deconvolution method for photon-limited speckle images is proposed. It is based on an iterative scheme consisting of object estimation, object modification and wavefront estimation. The object modification is implemented by constraining an object estimate so as to fit its Knox–Thompson spectral phases to measured ones. With regard to the wavefront estimation, a maximum-likelihood procedure is used. Computer simulations are conducted to investigate the performance of the method, and show successful results. The method is applied to observational speckle data of a binary star.

A study of the validity of the image deconvolution method on the basis of channelling theory for thicker crystals

The image deconvolution method in high-resolution electron microscopy is theoretically studied for the applications in materials science. The weak-phase object approximation is replaced by a new formulation of diffraction wave function based on the channelling theory [D. Van Dyck, M. Op de Beeck, Ultramicroscopy 64 (1996) 99], which is valid for rather thicker crystals. It is shown that in some special cases, the phases of two-dimensional structure factors can be reliably determined by the compensation for contrast transfer function on the images of thicker crystals. Furthermore, the projected atomic columns are always displayed at the correct positions in the deconvoluted images. In contrast to the weak phase object approximation, however, the projected column image intensities are non-linearly related to the projected atomic potential.

Examination of Atomic (Scanning) Force Microscopy Probe Tips with the Transmission Electron Microscope

Abstract: We have developed a method for the examination of atomic force microscopy (scanning force microscopy) tips using a high-resolution transmission electron microscope (TEM). The tips can be imaged in a nondestructive way, enabling one to observe the shape of an atomic force microscope probe in the vicinity of the apex with high resolution. We have obtained images of atomic force microscopy probes with a resolution on the order of 1 nm. The tips can be imaged repeatedly, so one can examine tips before and after use. We have found that the tip can become blunted with use, the rate of wear depending upon the sample and tip materials and the scanning conditions. We have also found that the tips easily accrue contamination. We have studied both commercially produced tips, as well as tips grown by electron beam deposition. Direct imaging in the TEM should prove useful for image deconvolution methods because one does not have to make any assumptions concerning the general shape of the tip profile.

Convolution and deconvolution with Gaussian kernel

In this paper we prove the following theorem. Suppose an imageI and an object Ω are related by the convolution equationI=Ω*δλF, whereδλF is a Gaussian kernel with widthλF. Suppose further that the imageI is expanded in a series of Gaussian derivatives asI=Σan▽nδΛ, whereδΛ is a Gaussian with widthΛ>λF, and where ∇represents then-th derivative ofδΛ. Then the object Ω is given by Ω = Σan∇nδλ, whereλ2-Λ2-λF2, and where the coefficientsan are exactly the coefficients obtained in the expansion of the imageI. The expansion in Gaussian derivatives can therefore be used to develop a simple and efficient deconvolution method for images which have been convolved with a Gaussian filter. We consider both one- and two-dimensional problems, and give a discussion of the error caused by truncation of the expansion of the image. We also give a two-dimensional numerical example which shows how our deconvolution method can be used in the restoration of digitized gray-scale images.

Restoration of blurred star field images by maximally sparse optimization

The problem of removing blur from, or sharpening, astronomical star field intensity images is discussed. An approach to image restoration that recovers image detail using a constrained optimization theoretic approach is introduced. Ideal star images may be modeled as a few point sources in a uniform background. It is argued that a direct measure of image sparseness is the appropriate optimization criterion for deconvolving the image blurring function. A sparseness criterion based on the l(p) is presented, and candidate algorithms for solving the ensuing nonlinear constrained optimization problem are presented and reviewed. Synthetic and actual star image reconstruction examples are presented to demonstrate the method's superior performance as compared with several image deconvolution methods.

Deconvolution of the Chemical Shift Effects in Proton Nuclear Magnetic Resonance Imaging

When the gradient strength is not strong enough to suppress the chemical shift effects, it is possible to apply a numerical deconvolution technique to remove the chemical shift artifacts. The Wiener filter and an apodization function are combined to produce an effective deconvolution method for the NMR images obtained by standard spin-echo NMR imaging technique. In the deconvolution process, an NMR spectrum is utilized which is taken at the same condition as the NMR image but with the phase encoding and the read gradients off.