Iterative Blind Deconvolution Algorithm Research Articles

Single voxel vascular transport functions of arteries, capillaries and veins; and the associated arterial input function in dynamic susceptibility contrast magnetic resonance brain perfusion imaging

PurposeThe composite vascular transport function of a brain voxel consists of one convolutional component for the arteries, one for the capillaries and one for the veins in the voxel of interest. Here, the goal is to find each of these three convolutional components and the associated arterial input function. Pharmacokinetic modellingThe single voxel vascular transport functions for arteries, capillaries and veins were all modelled as causal exponential functions. Each observed multipass tissue contrast function was as a first approximation modelled as the resulting parametric composite vascular transport function convolved with a nonparametric and voxel specific multipass arterial input function. Subsequently, the residue function was used in the true perfusion equation to optimize the three parameters of the exponential functions. Deconvolution methodsFor each voxel, the parameters of the three exponential functions were estimated by successive iterative blind deconvolutions using versions of the Lucy-Richardson algorithm. The final multipass arterial input function was then computed by nonblind deconvolution using the Lucy-Richardson algorithm and the estimated composite vascular transport function. ResultsSimulations showed that the algorithm worked. The estimated mean transit time of arteries, capillaries and veins of the simulated data agreed with the known input values. For real data, the estimated capillary mean transit times agreed with known values for this parameter. The nonparametric multipass arterial input functions were used to derive the associated map of the arrival time. The arrival time map of a healthy volunteer agreed with known arterial anatomy and physiology. ConclusionClinically important new voxelwise hemodynamic information for arteries, capillaries and veins separately can be estimated using multipass tissue contrast functions and the iterative blind Lucy-Richardson deconvolution algorithm.

Research on adaptive optics image restoration algorithm based on improved joint maximum a posteriori method

In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio (PSNR) and Laplacian sum (LS) value than the others. The research results have a certain application values for actual AO image restoration.

Super-Resolution using Interpolation and Wavelet Lifting Scheme

Due to the limitation of image capturing technique, we often get low resolution(LR) images. We can construct high resolution(HR) images from one or more LR images and this method is called Super Resolution(SR). In this paper we have proposed one lifting based super resolution algorithm which generate super resolution image from three rotated, noisy and blurred images. We have used HAAR wavelet lifting scheme and ASWDR encoding method. To remove noise and blur from original images we have applied Gaussian filter and Iterative Blind Deconvolution(IBD) algorithm.

自适应光学图像非对称图像迭代盲复原算法

自适应光学图像非对称图像迭代盲复原算法

Support vector regression based color image restoration in YUV color space

A support vector regression (SVR) based color image restoration algorithm is proposed. The test color images are firstly mapped into the YUV color space, and then SVR is applied to build up a theoretical model between the degraded images and the original one. Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given. Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterative blind deconvolution algorithm.

Restoration of motion blurred images

In most of the real life applications of imaging, the Point Spread Functions (PSFs) responsible for degrading the observed images are not known. Hence, PSF and image must be identified from the observed noisy blurred image. Here, we present a computationally simple iterative blind deconvolution algorithm that alternates between Fourier and spatial domain. During the iteration, the algorithm imposes some constraints making the results more accurate and visually more appealing than existing schemes. This method delivers good results for images having uniform as well as non-uniform background intensity. Experimental results are provided to validate the performance of the proposed scheme.

Enhanced Biggs–Andrews Asymmetric Iterative Blind Deconvolution

The main contribution of this paper is the introduction of a framework for estimation of multiple unknown blurs as well as their respective supports. Specifically, the Biggs---Andrews (B---A) multichannel iterative blind deconvolution (IBD) algorithm is modified to include the blur support estimation module and the asymmetry factor for the Richardson---Lucy (R---L) update-based IBD algorithm is calculated. A computational complexity assessment of the implemented modified IBD is made. Simulations conducted on real-world and synthetic images confirm the importance of accurate support estimation in the blind superresolution problem.

Restoration of Astronomical Images by Iterative Blind Deconvolution

view Abstract Citations (108) References (33) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Restoration of Astronomical Images by Iterative Blind Deconvolution Jefferies, Stuart M. ; Christou, Julian C. Abstract We have developed a modified version of the Iterative Blind Deconvolution (IBD) algorithm of Lane, applicable to different types of astronomical data. Besides using positivity, convolution, and support constraints, we have also applied band-limit, multiple image, and Fourier modulus constraints. By using all the available image constraining information, we are able to successfully recover object and point spread function information from noisy data. The algorithm's performance under controlled conditions using simulated data for a binary source object, a compact multiple quasi-point source object, and an extended object with low contrast are demonstrated. The ability of the algorithm to restore information beyond the conventional cutoff frequency is also demonstrated. Results are presented for infrared speckle imaging of (1) the nearby binary star Gl 914, which resolves the secondary component into two stars, and (2) the asteroid 4 Vesta. We also present results for "direct" imaging data in the form of a visible high-resolution image of Capella and an infrared adaptively corrected image of the Galactic center. Publication: The Astrophysical Journal Pub Date: October 1993 DOI: 10.1086/173208 Bibcode: 1993ApJ...415..862J Keywords: METHODS: DATA ANALYSIS; MINOR PLANETS; STARS: BINARIES: VISUAL; TECHNIQUES: IMAGE PROCESSING full text sources ADS | data products SIMBAD (3)

Iterative blind deconvolution algorithm applied to phase retrieval

The iterative blind deconvolution algorithm proposed by Ayers and Dainty [ Opt. Lett.13, 547 ( 1988)] and improved on by Davey et al. [ Opt. Commun.69, 353 ( 1989)] is applied to the problem of phase retrieval, which is a special case of the blind deconvolution problem. A close relationship between this algorithm and the error-reduction version of the iterative Fourier-transform phase-retrieval algorithm is shown analytically. The performance of the blind deconvolution algorithm is compared with the error-reduction and hybrid input–output versions of the iterative Fourier-transform algorithm by reconstruction experiments on real-valued, nonnegative images with and without noise.