Iterative Blind Deconvolution Research Articles

Richardson–Lucy Iterative Blind Deconvolution with Gaussian Total Variation Constraints for Space Extended Object Images

Richardson–Lucy Iterative Blind Deconvolution with Gaussian Total Variation Constraints for Space Extended Object Images

Iterative blind deconvolution method for dwell-time adjustment.

Negative values occur in the calculation results when a fast Fourier transform is used in the algorithm to calculate the dwell time via the dwell-time adjustment algorithm for optical element grinding. In the present study, a processing method for negative values is used in the Fourier iterative Ayers-Dainty (AD) algorithm. The method in which the range of the unit removal shape was adopted in the AD algorithm resulted in a smaller error and enabled the error to be minimized using the quantized dwell-time distribution output.

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.

Toeplitz-based blind deconvolution of underwater acoustic channels using wideband integrated dictionaries

In this paper, we propose a blind channel deconvolution method based on a sparse reconstruction framework exploiting a wideband dictionary under the (relatively weak) assumption that the transmitted signal may be assumed to be well modelled as a sum of sinusoids. Using a Toeplitz structured formulation of the received signal, we form an iterative blind deconvolution scheme, alternatively estimating the underwater impulse response and the transmitted waveform. The resulting optimization problems are convex, and we formulate a computationally efficient solver using the Alternating Direction Method of Multipliers (ADMM). We illustrate the performance of the resulting estimator using both simulated and measured underwater signals.

Design and evaluation of an accurate CNR-guided small region iterative restoration-based tumor segmentation scheme for PET using both simulated and real heterogeneous tumors.

Tumor delineation accuracy directly affects the effectiveness of radiotherapy. This study presents a methodology that minimizes potential errors during the automated segmentation of tumors in PET images. Iterative blind deconvolution was implemented in a region of interest encompassing the tumor with the number of iterations determined from contrast-to-noise ratios. The active contour and random forest classification-based segmentation method was evaluated using three distinct image databases that included both synthetic and real heterogeneous tumors. Ground truths about tumor volumes were known precisely. The volumes of the tumors were in the range of 0.49-26.34cm3, 0.64-1.52cm3, and 40.38-203.84cm3 respectively. Widely available software tools, namely, MATLAB, MIPAV, and ITK-SNAP were utilized. When using the active contour method, image restoration reduced mean errors in volumes estimation from 95.85 to 3.37%, from 815.63 to 17.45%, and from 32.61 to 6.80% for the three datasets. The accuracy gains were higher using datasets that include smaller tumors for which PVE is known to be more predominant. Computation time was reduced by a factor of about 10 in the smaller deconvolution region. Contrast-to-noise ratios were improved for all tumors in all data. The presented methodology has the potential to improve delineation accuracy in particular for smaller tumors at practically feasible computational times. Graphical abstract Evaluation of accurate lesion volumes using CNR-guided and ROI-based restoration method for PET images.

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.

<inline-formula> <tex-math notation="LaTeX">$\ell_0$ </tex-math> </inline-formula> Sparse Approximation of Coastline Inflection Method on FY-3C MWRI Data

The microwave radiation imager (MWRI) located onboard the FengYun-3C (FY-3C) satellite provides a considerable amount of critical information for numerical weather predictions. Obtaining accurate geolocation results from the FY-3C MWRI data is of great importance. In this letter, we improve the traditional coastline inflection method (CIM) and propose an $\ell _{0}$ sparse approximation model for geolocation error estimation and correction. Specifically, we propose using the jump point of the step function to estimate the true coastline point. This approach can characterize the geolocation errors more accurately than the CIM, which further improves the geolocation accuracy. In the theoretical part, we provide a complete solution to obtain the step function through an iterative blind deconvolution. For a practical use, we demonstrate the effectiveness of the proposed method for geolocation error estimation through quantitative results obtained on the FY-3C MWRI data. The experimental results show that the proposed method can achieve an improvement of up to 33.33% in the standard deviation of geolocation errors (approximately 0.00030) compared to the traditional CIM (approximately 0.00045). Furthermore, we also apply the proposed method to the FY-3C satellite and improve the geolocation accuracy of the MWRI data through geolocation error correction.

Estimating the Size of the Point Spread Function Using Fuzzy Logic for Blind Restoration of Satellite Image

Despite the considerable progress in the field of imaging, the acquired image can undergo certain degradations which are mainly summarized in blur and noise. The objective of the restoration is to estimate from the observed image an image as close as possible to the original image. The iterative blind deconvolution (IBD) can be used effectively when no information about the distortion is known. This algorithm starts with a random initial estimate of the point spread function (PSF) whose its size affects strongly the restoration process of the degraded image. In this paper, we have implemented a fuzzy inference system (FIS) to determine the size of the PSF through the examination of the blurred satellite image edges and the measurement of the blur width in pixels around an obviously sharp object. The obtained results are encouraging, which confirms the good performance of the proposed approach.

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.

Blur robust optical flow using motion channel

It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur. In this paper, we first investigate the blur parameterisation for video footage using near linear motion elements. We then combine a commercial 3D pose sensor with an RGB camera, in order to film video footage of interest together with the camera motion. We illustrate that this additional camera motion/trajectory channel can be embedded into a hybrid framework by interleaving an iterative blind deconvolution and warping based optical flow scheme. Our method yields improved accuracy within three other state-of-the-art baselines given our proposed ground truth blurry sequences; and several other realworld sequences filmed by our imaging system.

Blind Deconvolution in Quantitative Brain PET Studies with SUV Parametrization

Background Quantification in Positron Emission Tomography (PET) is essential for achieving high precision in diagnosis, physiological parameter estimation, disease staging as well as prognosis. Many difficulties are known to cause errors in quantification. Of these, PVE is primarily caused by the limited spatial resolution of the camera and causes values to appear lower then their actual magnitudes. Many of the proposed solutions require the determination of the point spread function (PSF) of the camera. Purpose The present work aims to address this problem, namely, the the recovery of the activity concentrations or SUV's when the PSF is unknown. A second objective was to focus on brain PET imaging where the lack of accurate quantification is known to have a significant impact in neurological and oncological studies. Methods and Materials We have used an iterative blind deconvolution method which does not require the PSF to be known a priori. We applied the algorithm on realistic brain PET images with known ground truth. These images are obtained by extracting anatomical models from T1-weighted MRI studies, assigning Time Activity Curves to brain regions based on real PET scans and then using Monte Carlo simulations for a known scanner model. Data are reconstructed using a standard 3D filtered back projection algorithm. These images (The PET SORTEO database) are available online. Results The deconvolved image of a patient image is given in Fig. 2. Experimentation with different iteration numbers and blur extent has resulted in recovery coefficients that approach 0.85 while the RC for the images before deconvolution were found to be as low as 0.57 for different brain regions. Conclusion Blind deconvolution with parameter optimization has resulted in significant improvement in the recovery coefficients (RC's). This may have a substantial effect on the diagnostic reliability of neurological and oncological diseases of the brain. Further work is planned for the evaluation of these effects.

Threshold based Approach for Image Blind Deconvolution

attractiveness in digital cameras, the digital image processing is getting more imperative nowadays. One of the most common problems facing with digital photography is noise and blurring that needs restoration. In this paper, we present a new method for image blind deconvolution (2). The Proposed Method employs threshold based image restoration technique in blind image deconvolution. The goal of this work is to restore the image from a noisy and blurred image where the blurring function is not known. The blur process can be formulated as the image takes convolution operation with the Gaussian noise. One of the basic blind deconvolution method is an iterative blind deconvolution method. (5), (31). Although Iterative Blind Deconvolution method can recover the image from blurred image, it is sensitive to initial estimation and computation time required is more. In order to decrease this computation time and better visual results than Iterative blind Deconvolution, we proposed a threshold based Blind image deconvolution algorithm.

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.

应用自解卷积和增量Wiener滤波实现迭代盲图像复原

An iterative blind image restoration algorithm based on Self-deconvolution and Incremental Wiener Filter(SDIWF-IBD) is proposed.The self-deconvolution estimation for a Point Spread Function(PSF) is applied to Iterative Blind Deconvolution(IBD) to estimating exactly the frequency domain of the PSF.The incremental Wiener filter is used in the image estimation of IBD to keep the algorithm convergence steady.To further control the convergency,an in-iterative acceleration is suggested to control the speed of algorithm convergence and reduce total external iteration.Experimental results indicate that more details are recovered in the restoration image with few distortions,and the algorithm is converged to a small error quickly.It concludes that the SDIWF-IBD algorithm has good restoration ability at a fast and controllable convergency speed,and is fit for applications in real-time.

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

Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy

In this work, we propose a novel method for the regularization of blind deconvolution algorithms. The proposed method employs example-based machine learning techniques for modeling the space of point spread functions. During an iterative blind deconvolution process, a prior term attracts the point spread function estimates to the learned point spread function space. We demonstrate the usage of this regularizer within a Bayesian blind deconvolution framework and also integrate into the latter a method for noise reduction, thus creating a complete blind deconvolution method. The application of the proposed algorithm is demonstrated on synthetic and real-world three-dimensional images acquired by a wide-field fluorescence microscope, where the need for blind deconvolution algorithms is indispensable, yielding excellent results.

Blind phase retrieval for aberrated linear shift-invariant imaging systems

We develop a means to reconstruct an input complex coherent scalar wavefield, given a through focal series (TFS) of three intensity images output from a two-dimensional (2D) linear shift-invariant optical imaging system with unknown aberrations. This blind phase retrieval technique unites two methods, namely (i) TFS phase retrieval and (ii) iterative blind deconvolution. The efficacy of our blind phase retrieval procedure has been demonstrated using simulated data, for a variety of Poisson noise levels.

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.

Strehl-constrained iterative blind deconvolution for post-adaptive-optics data

<i>Aims. <i/>We aim to improve blind deconvolution applied to post-adaptive-optics (AO) data by taking into account one of their basic characteristics, resulting from the necessarily partial AO correction: the Strehl ratio.<i>Methods. <i/>We apply a Strehl constraint in the framework of iterative blind deconvolution (IBD) of post-AO near-infrared images simulated in a detailed end-to-end manner and considering a case that is as realistic as possible.<i>Results. <i/>The results obtained clearly show the advantage of using such a constraint, from the point of view of both performance and stability, especially for poorly AO-corrected data. The proposed algorithm has been implemented in the freely-distributed and CAOS-based Software Package AIRY.

Adaptive Optics Image Restoration Based on Frame Selection and Multi-frame Blind Deconvolution

Restricted by the observational condition and the hardware, adaptive optics can only make a partial correction of the optical images blurred by atmospheric turbulence. A postprocessing method based on frame selection and multi-frame blind deconvolution is proposed for the restoration of high-resolution adaptive optics images. By frame selection we mean we first make a selection of the degraded (blurred) images for participation in the iterative blind deconvolution calculation, with no need of any a priori knowledge, and with only a positivity constraint. This method has been applied to the restoration of some stellar images observed by the 61-element adaptive optics system installed on the Yunnan Observatory 1.2m telescope. The experimental results indicate that this method can effectively compensate for the residual errors of the adaptive optics system on the image, and the restored image can reach the diffraction-limited quality.