Blind Image Restoration Algorithm Research Articles

Blind image restoration algorithm based on improved sparse Bayesian low dose CT

Blind image restoration algorithm based on improved sparse Bayesian low dose CT

Joint blur kernel estimation and CNN for blind image restoration

Convolutional neural networks (CNN) have shown its excellent performance in computer vision fields. Recently, they are successfully applied to image restoration. This paper proposes a joint blur kernel estimation and CNN method for blind image restoration. The blur kernel estimation is based on both blur support parameter estimation and blur type identification. An automatic feature line detection algorithm is presented for blur support parameter estimation and a dictionary learning algorithm is presented for the blur type identification. Once the blur kernel estimate is obtained, we use an effective CNN for iterative non-blind deconvolution, which is able to automatically learn image priors. Compared with current blind image restoration methods, the proposed joint method can obtain restored images under three types of unknown blur kernels. The experimental result shows that the proposed blur kernel estimation algorithm can provide high accuracy results. Furthermore, the proposed joint blur kernel estimation and CNN algorithm is superior to conventional blind image restoration algorithms in terms of restoration quality and computation time.

An Improved Image Ringing Evaluation Method with Weighted Sum of Gray Extreme Value

Blind image restoration algorithm usually produces ringing more obvious at the edges. Ringing phenomenon is mainly affected by noise, species of restoration algorithm, and the impact of the blur kernel estimation during restoration. Based on the physical mechanism of ringing, a method of evaluating the ringing on blind restoration images is proposed. The method extracts the ringing image overshooting and ripple region to make the weighted statistics for the regional gradient value. According to the weights set by multiple experiments, the edge information is used to characterize the details of the edge to determine the weight, quantify the seriousness of the ring effect, and propose the evaluation method of the ringing caused by blind restoration. The experimental results show that the method can effectively evaluate the ring effect in the restoration images under different restoration algorithms and different restoration parameters. The evaluation results are consistent with the visual evaluation results.

基于能量约束的自适应加权图像盲复原算法

基于能量约束的自适应加权图像盲复原算法

Fraction-order total variation blind image restoration based on L1-norm

• Propose a fraction-order total variation blind image restoration algorithm. • Introduce three auxiliary variables and semi-regularization method. • The performance of the proposed algorithm outperforms other previous algorithms. A fraction-order total variation blind image restoration algorithm based on L1-norm was proposed for restoring the images blurred by unknown point spread function (PSF) during imaging. According to the form of total variation, this paper introduced an arithmetic operator of fraction-order total variation and generated a mathematical model of cost. Semi-quadratic regularization was used to solve the model iteratively so that the solution of this algorithm became easier. This paper also analyzed the convergence of this algorithm and then testified its feasibility in theory. The experimental results showed the proposed algorithm can increase the PSNR of the restored image by 1 dB in relation to the first order total variation blind restoration method and Bayesian blind restoration method. The details in real blurred image were also pretty well restored. The effectiveness of the proposed algorithm revealed that it was practical in the blind image restoration.

Robust Image Restoration for Motion Blur of Image Sensors.

Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-adaptive structure extraction method based on the total variation (TV) to separate the reliable structures from textures and small details of a blurred image which may damage the kernel estimation and interim latent image restoration. Secondly, we combine the reliable structures with priors of the blur kernel, such as sparsity and continuity, by a two-step method with which noise can be removed during iterations of the estimation to improve the precision of the estimated blur kernel. Finally, we use a MR-based Wiener filter as the non-blind deconvolution algorithm to restore the final latent image. Experimental results demonstrate that our algorithm can restore large blur images with rich, small details effectively.

基于指数律分布的图像快速盲复原算法

基于指数律分布的图像快速盲复原算法

Analysis and performance of CMA blind deconvolution for image restoration

SummaryIn this paper we study the applicability of classical blind deconvolution methods such as constant modulus algorithm (CMA) for blind adaptive image restoration. The requirements such as the source to be white, uniformly distributed and zero mean, which yield satisfactory convergence in the data communication application context, are revisited in the image restoration context, where a linear deblur kernel needs to be blindly adapted to compensate for an unknown image blur kernel with the objective to recover a source ground truth image. Through analysis and performance studies, we show that the performance of CMA is adversely affected by the intrinsic spatial correlation of natural images and by any deviation of their distribution from being platykurtic. We also show that decorrelation techniques designed to overcome spatial correlation cannot be effectively applied to rectify CMA performance for blind adaptive image restoration. Copyright © 2014 John Wiley & Sons, Ltd.

盲图像复原研究现状

Blind image restoration is an approach to estimate both the original image and the point spread function from degraded images,when there is no or little knowledge of the point spread function of the degraded process. In this paper,the main algorithms of blind image restoration are reviewed and classified into four types,according to their theory origins and relationships,and analysis is made for all the types of blind image restoration algorithms and relevant improved algorithms. This will be helpful to better understand the theory of blind image restoration and to restore degraded images.

An Enhanced NAS-RIF Algorithm for Blind Image Restoration Based on Total Variation Regularization

In order to improve the quality of blind image restoration, we propose an algorithm which combines Non-negativity and Support constraint Recursive Inverse Filtering (NAS-RIF) and adaptive total variation regularization. In the proposed algorithm, the total variation regularization constraint term is added in the NAS-RIF algorithm cost function. The majorization-minimization approach and conjugate gradient iterative algorithm are adopted to improve the convergence speed. We do the simulation experiments for the blurred classic test image which is added additive random noise. Experimental results show that the restoration effect of our algorithm is better than the spatially adaptive Tikhonov regularization method and the NAS-RIF spatially adaptive regularization algorithm, while the value of improvement of signal to noise ratio (ISNR) has improved.

Analysis and application of a novel fast algorithm for 2-D ARMA model parameter estimation

In this paper, we analyze a novel algorithm for 2-D ARMA model parameter estimation in the presence of noise and then develop a fast and efficient blind image restoration algorithm. We show that the novel algorithm can minimize a quadratic convex optimization problem and has a lower computational complexity than the conventional algorithms. As a result, the novel algorithm involves no convergence and local minimum issue. Moreover, the proposed blind image restoration algorithm can overcome the local minimization problem. Computed results confirm that the novel algorithm can more quickly obtain more accurate estimates than the conventional algorithms in the presence of noise.

Blind Image Restoration Based on Signal-to-Noise Ratio and Gaussian Point Spread Function Estimation

In order to improve the quality of restored image, a blind image restoration algorithm is proposed, in which both the Signal-to-Noise Ratio (SNR) and the Gaussian Point Spread Function (PSF) of the degraded image are estimated. Firstly, the SNR of the degraded image is estimated through local deviation method. Secondly, the PSF of the degraded image is estimated through error-parameter method. Thirdly, Utilizing the estimated SNR and PSF, high resolution image is restored through Wiener filtering restoration algorithm. Experimental results show that the quality and peak signal-to-noise of the restored image are better around the real value and justify the fact that the SNR an-d PSF estimation plays great important part in blind image restoration.

Neural Network Blind Equalization Algorithm Applied in Medical CT Image Restoration

A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI). In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically; meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.

Blind Image Restoration Algorithm Iteratively Using Wavelet Denoising and Total Variation Regularization

盲图像恢复就是在点扩散函数未知情况下从降质观测图像恢复出原图像。该文提出了一种交替使用小波去噪和全变差正则化的盲图像恢复算法。观测模型首先被分解成两个相互关联的子模型，这种分解转化盲恢复问题成为图像去噪和图像恢复两个问题，可以交替采用图像去噪和图像恢复算法求解。模糊辨识阶段，使用全变差正则化算法估计点扩散函数；图像恢复阶段，使用小波去噪和全变差正则化相结合的算法恢复图像。实验结果和与其它方法的比较表明该文算法能够获得更好的恢复效果。

A Kullback–Leibler Divergence Approach to Blind Image Restoration

A new algorithm for maximum-likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the linear image degradation model and a desired family of probability distributions constrained to be concentrated on the observed data. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.

Satellite image blind restoration based on surface fitting and iterative Multishrinkage method in redundant wavelet domain

In view of the non-unique, unstable and even divergent properties of the solution of iterative blind restoration, a non-iterative blind image-restoration algorithm was proposed, which included MTF estimation step and image-restoration step. In the MTF estimation stage, based on every degradation process of satellite imaging chain, a combined parametric model of MTF was given and used to fit the surface of normalized logarithmic amplitude spectrum of degraded image. In the image-restoration stage, a multivariate statistical model was introduced that can perfectly characterize the dependency of the neighboring wavelet coefficients and of reference coefficient and its parent. During the image-restoration, optimization-transfer method was adopted to decompose the image-restoration procedure into two simple steps: Landweber iteration and Multishrinkage denoising. In the numerical experiment, the comparison of restored panchromatic SPOT-5 (5 m) images with corresponding panchromatic SPOT-5 (2.5 m) image and higher resolution aerial image from the Google Earth software shows that the proposed algorithm can significantly restore some detailed information and effectively inhibit noise.

A High Resolution Color Image Restoration Algorithm for Thin TOMBO Imaging Systems

In this paper, we present a blind image restoration algorithm to reconstruct a high resolution (HR) color image from multiple, low resolution (LR), degraded and noisy images captured by thin (< 1mm) TOMBO imaging systems. The proposed algorithm is an extension of our grayscale algorithm reported in [1] to the case of color images. In this color extension, each Point Spread Function (PSF) of each captured image is assumed to be different from one color component to another and from one imaging unit to the other. For the task of image restoration, we use all spectral information in each captured image to restore each output pixel in the reconstructed HR image, i.e., we use the most efficient global category of point operations. First, the composite RGB color components of each captured image are extracted. A blind estimation technique is then applied to estimate the spectra of each color component and its associated blurring PSF. The estimation process is formed in a way that minimizes significantly the interchannel cross-correlations and additive noise. The estimated PSFs together with advanced interpolation techniques are then combined to compensate for blur and reconstruct a HR color image of the original scene. Finally, a histogram normalization process adjusts the balance between image color components, brightness and contrast. Simulated and experimental results reveal that the proposed algorithm is capable of restoring HR color images from degraded, LR and noisy observations even at low Signal-to-Noise Energy ratios (SNERs). The proposed algorithm uses FFT and only two fundamental image restoration constraints, making it suitable for silicon integration with the TOMBO imager.

Satellite image blind restoration based on surface fitting and multivariate model

Owing to the blurring effect from atmosphere and camera system in the satellite imaging, a blind image restoration algorithm is proposed which includes the modulation transfer function (MTF) estimation and the image restoration. In the MTF estimation stage, based on every degradation process of satellite imaging-chain, a combined parametric model of MTF is given and used to fit the surface of normalized logarithmic amplitude spectrum of degraded image. In the image restoration stage, a maximum a posteriori (MAP) based edge-preserving image restoration method is presented which introduces multivariate Laplacian model to characterize the prior distribution of wavelet coefficients of original image. During the image restoration, in order to avoid solving high nonlinear equations, optimization transfer algorithm is adopted to decompose the image restoration procedure into two simple steps: Landweber iteration and wavelet thresholding denoising. In the numerical experiment, the satellite image restoration results from SPOT-5 and high resolution camera (HR) of China & Brazil earth resource satellite (CBERS-02B) ane compared, and the proposed algorithm is superior in the image edge preservation and noise inhibition.

基于小波变换和非负支撑域递归逆滤波算法的盲目图像复原

基于小波变换和非负支撑域递归逆滤波算法的盲目图像复原

Blind Image Restoration Based on the Recursive Prediction Error Method

We develop a 2-D image restoration and identification algorithm only using the blurred noisy images without any a priori knowledge of original images. A generalized semicausal stchastic image models are identified by applying the ARMA adaptive algorithm combined with the spectral factorization. Finally, we show experimentally the effectiveness of our proposed blind image restoration algorithm by using monochromatic real images.