Multichannel blind CT image restoration via variable splitting and alternating direction method

This work derives the multichannel Bussgang restoration algorithm for blind image restoration problems. In its basic outline, the derived Bussgang restoration algorithm is based on iteratively filtering the measurements by means of a bank of FIR filters, and updating the filter-bank on the base of a nonlinear estimate of the original image. The filters are updated by solving a linear system (multichannel normal equations) whose coefficients' matrices depend on the cross-correlations between the measurements and a nonlinear estimate of the original image, this latter obtained using a Bayesian criterion. Experimental results pertaining to restoration of motion blurred text images and out-of-focus star field images are reported.

Blind point-source image restoration refers to the problem of high resolution recovery of point-like sources which are blurred by an unknown point spread function (PSF). Applications include astronomical star field localization, magnetoencephalogram brain current imaging, and seismic deconvolution. This paper shows that with suitable constraints the problem can be cast in the language of subspace decomposition as used in blind signal copy algorithms for digital wireless communications. Assuming a separable PSF, we propose a deterministic, non-iterative ESPRIT-like solution to the restoration problem. We next extend the algorithm to non-separable PSFs by approximating them as a series expansion of a few separable components.

Previous work has demonstrated the effectiveness of the expectation-maximization algorithm to restore noisy and blurred single- channel images and simultaneously identify its blur. In addition, a gen- eral framework for processing multichannel images using single-channel techniques has been developed. The authors combine and extend the two approaches to the simultaneous blur identification and restoration of multichannel images. Explicit equations for that purpose are developed for the general case when cross-channel degradations are present. An important difference from the single-channel problem is that the cross power spectra are complex quantities, which further complicates the analysis of the algorithm. The proposed algorithm is very effective at restoring multichannel images, as is demonstrated experimentally.

Blind image restoration, which restores a clear image from a single blurry image, is a difficult process of estimating two unknowns: a point-spread function (PSF) and an ideal image. In this paper, we use a learning-type restoration method that learns a sharp image without blur and an image that contains blur, and supposes the restoration of blurred images that occur especially when shooting fast-moving objects such as cars. We study the improvement of the image quality of the restored image by examining the training image used for the image.

For original article see Galatsanos et al. (IEEE Trans. Signal Processing, vol.39, p.2222-36, Oct. 1991). In this correspondence, we give the correct matrix formulation arising from the constrained optimization of the least squares restoration of multichannel images in Galatsanos et al.

Retinal images are often degraded with a blur that varies across the field view. Because traditional deblurring algorithms assume the blur to be space-invariant they typically fail in the presence of space-variant blur. In this work we consider the blur to be both unknown and space-variant. To carry out the restoration, we assume that in small regions the space-variant blur can be approximated by a space-invariant point-spread function (PSF). However, instead of deblurring the image on a per-patch basis, we extend individual PSFs by linear interpolation and perform a global restoration. Because the blind estimation of local PSFs may fail we propose a strategy for the identification of valid local PSFs and perform interpolation to obtain the space-variant PSF. The method was tested on artificial and real degraded retinal images. Results show significant improvement in the visibility of subtle details like small blood vessels.

This study addresses the blind image deconvolution which uses only blurred image and less point spread function (PSF) information to restore the original image. To identify the blind image it is a very important step for restoring the image. Therefore, the first step is to look for PSF model. In this paper, particle swarm optimization (PSO) is utilized to seek the unknown PSF. The objective function is based on the wavelet transform. It can identify the parameters of PSF exactly. Finally, the feasibility and validity of proposed algorithm are demonstrated by several simulations.

In this paper, we propose a blind method that rapidly restores blurred images using local patches. In this method, a portion of the blurred image is used for PSF (point spread function) estimation. In addition, we propose an automatic PSF size calculation algorithm that generates an autocorrelation map (auto map). Experimental results show that our proposed method generates accurate de-blurred images, and processing time is significantly lower than that of the conventional de-blurring method.

We address the reconstruction problem of a high resolution image from its undersampled measurements across multiple FIR channels with unknown response. Our method consists of two stages: blind multi-input multi-output (MIMO) deconvolution using FIR filters and blind separation of mixed polyphase components. The proposed deconvolution method is based on the mutually referenced equalizers (MRE) algorithm previously developed for blind equalization in digital communications. For sources separation, a method is proposed for separating mixed polyphase components of a bandlimited signal. The existing blind source separation algorithms assume that the source signals are either independent or uncorrelated, which is not the case when the sources are polyphase components of a bandlimited signal. Simulation results on artificial and photographic images are given.

In this paper, we develop a multichannel image restoration algorithm using compound Gauss-Markov random fields (CGMRF) models. The line process in the CGMRF allows the channels to share important information regarding the objects present in the scene. In order to estimate the underlying multichannel image, two new iterative algorithms are presented and their convergence is established. They can be considered as extensions of the classical simulated annealing and iterative conditional methods. Experimental results with color images demonstrate the effectiveness of the proposed approaches.

Blind image restoration is a great part of image processing, which refers to the technology field that study how to restore the original image from the observed image without sufficient prior knowledge about the degradation process. Recently, as a novel method for blind source separation based on high statistics, Independent Component Analysis (ICA)'s computation algorithms and applications are widely studied. In this paper, we present a Non-negative ICA algorithm which is used to the blind image restoration with the constraint of non-negativity. Besides this, through a matrix of de-correlation, it can reduce the requirements to the independence of the source signals in ICA algorithm. Through some computer simulation and experiments for real images, we show the effectiveness of the presented method.

Reducing radiation dose and enhancing imaging quality of 4DCT for radiation therapy using iterative reconstruction algorithms

PurposeHow to model blind image deblurring that arises when a camera undergoes ego-motion while observing a static and close scene. In particular, this paper aims to detail how the blurry image can be restored under a sequence of the linear model of the point spread function (PSF) that are derived from the 6-degree of freedom (DOF) camera’s accurate path during the long exposure time.Design/methodology/approachThere are two existing techniques, namely, an estimation of the PSF and a blind image deconvolution. Based on online and short-period inertial measurement unit (IMU) self-calibration, this motion path has discretized a sequence of the uniform speed of 3-DOF rectilinear motion, which unites with a 3-DOF rotational motion to form a discrete 6-DOF camera’s path. These PSFs are evaluated through the discrete path, then combine with a blurry image to restoration through deconvolution.FindingsThis paper describes to build a hardware attachment, which is composed of a consumer camera, an inexpensive IMU and a 3-DOF motion mechanism to the best of the knowledge, together with experimental results demonstrating its overall effectiveness.Originality/valueFirst, the paper proposes that a high-precision 6-DOF motion platform periodically adjusts the speed of a three-axis rotational motion and a three-axis rectilinear motion in a short time to compensate the bias of the gyroscope and the accelerometer. Second, this paper establishes a model of 6-DOF motion and emphasizes on rotational motion, translational motion and scene depth motion. Third, this paper addresses a novel model of the discrete path that the motion during long exposure time is discretized at a uniform speed, then to estimate a sequence of PSFs.

As a major branch and research focus in the image processing field, the study on image restoration is always of practical significance and application value. Blind image restoration has always been one of the most difficult problems in image restoration. In view of the image motion blurring induced by the relative motion between the camera and the subject, we present a blind image restoration method based on guided filter. We first estimate the point spread function by using the iteration algorithm in the frequency domain. And then, because the guided filter has the edge-preserving smoothing property, we restore the target image by the non-blind image restoration algorithm based on the guided filter. By iterating the above two steps, we can obtain the original clear image. In order to verify the effectiveness of the proposed algorithm, we give several groups of experiments. Experimental results show that the proposed algorithm can not only effectively eliminate the noise and suppress the ringing, but also well preserve the edge and texture details. Therefore, the proposed algorithm can restore the image with high quality.

In this paper, we propose a novel technique for blind image restoration and resolution enhancement based on radial basis function (RBF) neural network. The RBF network gives a solution of the regularization problem often seen in function estimation with certain standard smoothness functional used as stabilizers. A RBF network model is designed to represent the observed image. In this model, the number and distribution of the centers (which are set to the pixels of the observed image) are fixed. In addition, network output is set to the observed image pixel gray scale value. The RBF plays a role of point spread function. The technique can also be applied to image resolution enhancement by generating an interpolated image from the low resolution version. Experimental results show that the learning algorithm can effectively estimate the model parameters and the established neural network model has a high fidelity in representing an image. It is believed that the proposed neural network model provides a valuable tool for image restoration and resolution enhancement and holds promises to improve the quality and efficiency of image processing.