Iterative deblurring for CT metal artifact reduction

Iterative deblurring methods using the expectation maximization (EM) formulation and the algebraic reconstruction technique (ART), respectively, are adapted for metal artifact reduction in medical computed tomography (CT). In experiments with synthetic noise-free and additive noisy projection data of dental phantoms, it is found that both simultaneous iterative algorithms produce superior image quality as compared to filtered backprojection after linearly fitting projection gaps. Furthermore, the EM-type algorithm converges faster than the ART-type algorithm in terms of either the I-divergence or Euclidean distance between ideal and reprojected data in the authors' simulation. Also, for a given iteration number, the EM-type deblurring method produces better image clarity but stronger noise than the ART-type reconstruction. The computational complexity of EM- and ART-based iterative deblurring is essentially the same, dominated by reprojection and backprojection. Relevant practical and theoretical issues are discussed.     

Disparity-based space-variant image deblurring

Obtaining a good-quality image requires exposure to light for an appropriate amount of time. If there is camera or object motion during the exposure time, the image is blurred. To remove the blur, some recent image deblurring methods effectively estimate a point spread function (PSF) by acquiring a noisy image additionally, and restore a clear latent image with the PSF. Since the groundtruth PSF varies with the location, a blockwise approach for PSF estimation has been proposed. However, the block to estimate a PSF is a straightly demarcated rectangle which is generally different from the shape of an actual region where the PSF can be properly assumed constant. We utilize the fact that a PSF is substantially related to the local disparity between two views. This paper presents a disparity-based method of space-variant image deblurring which employs disparity information in image segmentation, and estimates a PSF, and restores a latent image for each region. The segmentation method firstly over-segments a blurred image into sufficiently many regions based on color, and then merges adjacent regions with similar disparities. Experimental results show the effectiveness of the proposed method.     

Spiral CT image deblurring for cochlear implantation

Cochlear implantation is the standard treatment for profound hearing loss, Preimplantation and postimplantation spiral computed tomography (CT) is essential in several key clinical and research aspects. The maximum image resolution with commercial spiral CT scanners is insufficient to define clearly anatomical features and implant electrode positions in the inner ear, In this paper, the authors develop an expectation maximization (EM)-like iterative deblurring algorithm to achieve spiral CT image super-resolution for cochlear implantation, assuming a spatially invariant linear spiral CT system with a three-dimensional (3-D) separable Gaussian point spread function (PSF). The authors experimentally validate the 3-D Gaussian blurring model via phantom measurement and profile fitting. The imaging process is further expressed as convolution of an isotropic 3-D Gaussian PSF and a blurred underlying volumetric image. Under practical conditions, an oblique reconstructed section is approximated as convolution of an isotropic two dimensional (2-D) Gaussian PSF and the corresponding actual cross section. The spiral CT image deblurring algorithm is formulated with sieve and resolution kernels for suppressing noise and edge artifacts. A typical cochlear cross section is used for evaluation, demonstrating a resolution gain up to 30%-40% according to the correlation criterion. Physical phantoms, preimplantation and postimplantation patients are reconstructed into volumes of 0.1-mm cubic voxels. The patient images are digitally unwrapped along the central axis of the cochlea and the implanted electrode array respectively, then oblique sections orthogonal to the central axis formed. After deblurring, representation of structural features is substantially improved in all the cases     

Sparse Poisson noisy image deblurring

Deblurring noisy Poisson images has recently been a subject of an increasing amount of works in many areas such as astronomy and biological imaging. In this paper, we focus on confocal microscopy, which is a very popular technique for 3-D imaging of biological living specimens that gives images with a very good resolution (several hundreds of nanometers), although degraded by both blur and Poisson noise. Deconvolution methods have been proposed to reduce these degradations, and in this paper, we focus on techniques that promote the introduction of an explicit prior on the solution. One difficulty of these techniques is to set the value of the parameter, which weights the tradeoff between the data term and the regularizing term. Only few works have been devoted to the research of an automatic selection of this regularizing parameter when considering Poisson noise; therefore, it is often set manually such that it gives the best visual results. We present here two recent methods to estimate this regularizing parameter, and we first propose an improvement of these estimators, which takes advantage of confocal images. Following these estimators, we secondly propose to express the problem of the deconvolution of Poisson noisy images as the minimization of a new constrained problem. The proposed constrained formulation is well suited to this application domain since it is directly expressed using the antilog likelihood of the Poisson distribution and therefore does not require any approximation. We show how to solve the unconstrained and constrained problems using the recent alternating-direction technique, and we present results on synthetic and real data using well-known priors, such as total variation and wavelet transforms. Among these wavelet transforms, we specially focus on the dual-tree complex wavelet transform and on the dictionary composed of curvelets and an undecimated wavelet transform.     

An adaptive Gaussian model for satellite image deblurring

The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem, which can be regularized within a Bayesian context by using an a priori model of the reconstructed solution. Since real satellite data show spatially variant characteristics, we propose here to use an inhomogeneous model. We use the maximum likelihood estimator (MLE) to estimate its parameters and we show that the MLE computed on the corrupted image is not suitable for image deconvolution because it is not robust to noise. We then show that the estimation is correct only if it is made from the original image. Since this image is unknown, we need to compute an approximation of sufficiently good quality to provide useful estimation results. Such an approximation is provided by a wavelet-based deconvolution algorithm. Thus, a hybrid method is first used to estimate the space-variant parameters from this image and then to compute the regularized solution. The obtained results on high resolution satellite images simultaneously exhibit sharp edges, correctly restored textures, and a high SNR in homogeneous areas, since the proposed technique adapts to the local characteristics of the data.     

Vector median deblurring filter for colour image restoration

A new technique is described which couples median filtering and image deblurring techniques to filter noisy images without introducing defocusing side effects. To deal with colour images a vector median filtering procedure is proposed. Using this procedure a better edge preserving filter is obtained which does not introduce new colours. The deblurring operation is performed componentwise by fitting an ARMA model to the image. The AR part of the model estimates the image and the MA part estimates the blurring function. Finally, the MA part is inverted and applied to remove the blur introduced by the median filter.<>     

Split Bregmanized anisotropic total variation model for image deblurring

In this paper, an effective image deblurring model is proposed to preserve sharp image edges by suppressing the stair-casing arising in the total variation (TV) based method by using the anisotropic total variation. To solve the difficult L1 norm problems, the split Bregman iteration is employed. Several synthetic degraded images are used for experiments. Comparison results are also made with total variation and nonlocal total variation based method. Experimental results show that the proposed method not only is robust to noise and different blur kernels, but also performs well on blurring images with more detailed textures, and the stair-casing effect is well suppressed.     

Improved image deblurring based on salient-region segmentation

Image deblurring techniques play important roles in many image processing applications. As the blur varies spatially across the image plane, it calls for robust and effective methods to deal with the spatially-variant blur problem. In this paper, a Saliency-based Deblurring (SD) approach is proposed based on the saliency detection for salient-region segmentation and a corresponding compensate method for image deblurring. We also propose a PDE-based deblurring method which introduces an anisotropic Partial Differential Equation (PDE) model for latent image prediction and employs an adaptive optimization model in the kernel estimation and deconvolution steps. Experimental results demonstrate the effectiveness of the proposed algorithm.     

A spatially adaptive nonparametric regression image deblurring.

We propose a novel nonparametric regression metthod for deblurring noisy images. The method is based on the local polynomial approximation (LPA) of the image and the paradigm of intersecting confidence intervals (ICI) that is applied to define the adaptive varying scales (window sizes) of the LPA estimators. The LPA-ICI algorithm is nonlinear and spatially adaptive with respect to smoothness and irregularities of the image corrupted by additive noise. Multiresolution wavelet algorithms produce estimates which are combined from different scale projections. In contrast to them, the proposed ICI algorithm gives a varying scale adaptive estimate defining a single best scale for each pixel. In the new algorithm, the actual filtering is performed in signal domain while frequency domain Fourier transform operations are applied only for calculation of convolutions. The regularized inverse and Wiener inverse filters serve as deblurring operators used jointly with the LPA-design directional kernel filters. Experiments demonstrate the state-of-art performance of the new estimators which visually and quantitatively outperform some of the best existing methods.     

BM3D frames and variational image deblurring

A family of the block matching 3-D (BM3D) algorithms for various imaging problems has been recently proposed within the framework of nonlocal patchwise image modeling , . In this paper, we construct analysis and synthesis frames, formalizing BM3D image modeling, and use these frames to develop novel iterative deblurring algorithms. We consider two different formulations of the deblurring problem, i.e., one given by the minimization of the single-objective function and another based on the generalized Nash equilibrium (GNE) balance of two objective functions. The latter results in the algorithm where deblurring and denoising operations are decoupled. The convergence of the developed algorithms is proved. Simulation experiments show that the decoupled algorithm derived from the GNE formulation demonstrates the best numerical and visual results and shows superiority with respect to the state of the art in the field, confirming a valuable potential of BM3D-frames as an advanced image modeling tool.     

A joint estimation approach for two-tone image deblurring by blind deconvolution

A new statistical method is proposed for deblurring two-tone images, i.e., images with two unknown grey levels, that are blurred by an unknown linear filter. The key idea of the proposed method is to adjust a deblurring filter until its output becomes two tone. Two optimization criteria are proposed for the adjustment of the deblurring filter. A three-step iterative algorithm (TSIA) is also proposed to minimize the criteria. It is proven mathematically that by minimizing either of the criteria, the original (nonblurred) image, along with the blur filter, will be recovered uniquely (only with possible scale/shift ambiguities) at high SNR. The recovery is guaranteed not only for i.i.d. images but also for correlated and nonstationary images. It does not require a priori knowledge of the statistical parameters or the tone values of the original image; neither does it require a priori knowledge of the phase or other special information (e.g., FIR, symmetry, nonnegativity, etc.) about the blur filter. Numerical experiments are carried out to test the method on synthetic and real images.     

Robust image deblurring with an inaccurate blur kernel

Most existing nonblind image deblurring methods assume that the blur kernel is free of error. However, it is often unavoidable in practice that the input blur kernel is erroneous to some extent. Sometimes, the error could be severe, e.g., for images degraded by nonuniform motion blurring. When an inaccurate blur kernel is used as the input, significant distortions will appear in the image recovered by existing methods. In this paper, we present a novel convex minimization model that explicitly takes account of error in the blur kernel. The resulting minimization problem can be efficiently solved by the so-called accelerated proximal gradient method. In addition, a new boundary extension scheme is incorporated in the proposed model to further improve the results. The experiments on both synthesized and real images showed the efficiency and robustness of our algorithm to both the image noise and the model error in the blur kernel.     

Robust blind motion deblurring using near-infrared flash image

In light-limited situations, camera motion blur is one of the prime causes for poor image quality. Recovering the blur kernel and latent image from the blurred observation is an inherently ill-posed problem. In this paper, we introduce a hand-held multispectral camera to capture a pair of blurred image and Near-InfraRed (NIR) flash image simultaneously and analyze the correlation between the pair of images. To utilize the high-frequency details of the scene captured by the NIR-flash image, we exploit the NIR gradient constraint as a new type of image regularization, and integrate it into a Maximum-A-Posteriori (MAP) problem to iteratively perform the kernel estimation and image restoration. We demonstrate our method on the synthetic and real images with both spatially invariant and spatially varying blur. The experiments strongly support the effectiveness of our method to provide both accurate kernel estimation and superior latent image with more details and fewer ringing artifacts.     

Iterative Wiener filters for image restoration

The iterative Wiener filter, which successively uses the Wiener-filtered signal as an improved prototype to update the covariance estimates, is investigated. The convergence properties of this iterative filter are analyzed. It has been shown that this iterative process converges to a signal which does not correspond to the minimum mean-squared-error solution. Based on the analysis, an alternate iterative filter is proposed to correct for the convergence error. The theoretical performance of the filter has been shown to give minimum mean-squared error. In practical implementation when there is unavoidable error in the covariance computation, the filter may still result in undesirable restoration. Its performance has been investigated and a number of experiments in a practical setting were conducted to demonstrate its effectiveness     

Robust penalty-weighted deblurring via kernel adaption using single image

Image blind deconvolution is well known as a challenging, ill-posed problem due to the uncertainty of the blur kernel and the noise condition. Based on our observations, blind deconvolution algorithms tend to generate disconnected and noisy blur kernels, which would yield a serious ringing effect in the restored image if the input image is noisy. Therefore, there is still room for further improvement, especially for noisy images captured under poor illumination conditions. In this paper, we propose a robust blind deconvolution algorithm by adopting a penalty-weighted anisotropic diffusion prior. On one hand, the anisotropic diffusion prior effectively eliminates the discontinuity in the blur kernel caused by the noisy input image during the process of kernel estimation. On the other hand, the weighted penalizer reduces the speckle noise of the blur kernel, thus improving the quality of the restored image. The effectiveness of the proposed algorithm is verified by both synthetic and real images with defocused or motion blur.     

Image-pair-based deblurring with spatially varying norms and noisy image updating

This paper presents a deblurring method that effectively restores fine textures and details, such as a tree’s leaves or regular patterns, and suppresses noises in flat regions using consecutively captured blurry and noisy images. To accomplish this, we used a method that combines noisy image updating with one iteration and fast deconvolution with spatially varying norms in a modified alternating minimization scheme. The captured noisy image is first denoised with a nonlocal means (NL-means) denoising method, and then fused with a deconvolved version of the captured blurred image on the frequency domain, to provide an initially restored image with less noise. Through a feedback loop, the captured noisy image is directly substituted with the initially restored image for one more NL-means denoising, which results in an upgraded noisy image with clearer outlines and less noise. Next, an alpha map that stores spatially varying norm values, which indicate local gradient priors in a maximum-a-posterior (MAP) estimation, is created based on texture likelihoods found by applying a texture detector to the initially restored image. The alpha map is used in a modified alternating minimization scheme with the pair of upgraded noisy images and a corresponding point spread function (PSF) to improve texture representation and suppress noises and ringing artifacts. Our results show that the proposed method effectively restores details and textures and alleviates noises in flat regions.     

Characterization and deblurring of lateral crosstalk in CMOS image sensors

Lateral crosstalk in CMOS imaging arrays deter effective utilization of small pixel sizes (e.g., < 5.0 /spl mu/m /spl times/ 5.0 /spl mu/m) now permitted by technology scaling. A simple measurement setup for empirical characterization of lateral crosstalk in CMOS image sensors is presented. A demonstration of deblurring operations based on the obtained blur model of lateral crosstalk is also provided. Several well-known linear deconvolution filters are employed in the demonstration. The tradeoffs in sharpness restoration, high-frequency noise amplification, and the intensity clipping effect in the design of linear deblurring operation for the application of lateral crosstalk are illustrated.