Adaptive iterative image restoration with reduced computational load (original) (raw)

Frequency-domain adaptive iterative image restoration and evaluation of the regularization parameter

1994

Abstract. An important consideration in regularized image restoration is the evaluation of the regularization parameter. Various techniques exist in the literature for the evaluation of this parameter, which depend on the assumed prior knowledge about the problem. These techniques eval-uate the regularization parameter either at a separate preprocessing step or by iterating based on the completely restored image, therefore requiring many restorations of the image with different values of the re-gularization parameter.

Simultaneous iterative image restoration and evaluation of the regularization parameter

IEEE Transactions on Signal Processing, 1992

A nonlinear regularized iterative image restoration algorithm is proposed, according to which only the noise variance is assumed to be known in advance. The algorithm results from a set theoretic regularization approach, where a bound of the stabilizing functional, and therefore the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the algorithm are derived

An adaptive algorithm for image restoration using combined penalty functions

Pattern Recognition Letters, 2006

In this paper, we present an adaptive gradient based method to restore images degraded by the effects of both noise and blur. The approach combines two penalty functions. The first derivative of the Canny operator is employed as a roughness penalty function to improve the high frequency information content of the image and a smoothing penalty term is used to remove noise. An adaptive algorithm is used to select the roughness and smoothing control parameters. We evaluate our approach using the Richardson-Lucy EM algorithm as a benchmark. The results highlight some of the difficulties in restoring blurred images that are subject to noise and show that in this case an algorithm that uses a combined penalty function is able to produce better quality results.

Restoration of Natural Images using Iterative Global and Local Adaptive Learning Scheme

Image restoration is a process of restoring the image from a damaged condition due to natural noise or by any stack of operations on the image. In this paper, we found a way to reduce the effect of noise on images using the combination of sparse learning approach with the help of neural networks. To make the Proposed system effective, initially, some images were trained, which are low noised and are natural and then by using residual internal and external priors network helps in restoring the damaged image. In this paper, we opted for various noised images such as Gaussian noised images, CCD and CMOS noised images for the restoration process. Purposefully we are unifying Sparse learning approach with neural networks and SVD to obtain a better-restored image from the effect of noises. We have tested our proposed approach on various image datasets and made a clear notation of working very extensively when compared with existing schemes. On an average, SSIM and PSNR metrics obtained are 0.9635 and 43.5dB, respectively.

Iterative Methods for Image Restoration by

2013

Although image restoration methods based on spectral filtering techniques are very efficient, they can be applied only to problems with fairly simple spatially invariant blurring operators. Iterative methods, however, are much more flexible; they can be very efficient for spatially invariant as well as spatially variant blurs, they can incorporate a variety of regularization techniques and boundary conditions, and they can more easily incorporate additional constraints, such as nonnegativity. This chapter describes a variety of iterative methods used in image restoration, with a particular emphasis on efficiency, convergence behavior, and implementation. Discussion

Iterative evaluation of the regularization parameter in regularized image restoration

1992

Abstract In this paper a nonlinear regularized iterative image restoration algorithm is proposed, according to which no prior knowledge about the noise variance is assumed. The algorithm results from a set-theoretic regularization approach, where bounds of the stabilizing functional and the noise variance, which determine the regularization parameter, are updated at each iteration step.

An Iterative Conjugate Gradient Regularization Method for Image Restoration

2009

Image restoration is an ill-posed inverse problem, which has been introduced the regularization method to suppress over-amplification. In this paper, we propose to apply the iterative regularization method to the image restoration problem and present a nested iterative method, called iterative conjugate gradient regularization method. Convergence properties are established in detail. Based on (6), we also simultaneously determine the regularization parameter based on the restored image at each step. Simulation results show that the proposed iterative regularization method is feasible and effective for image restoration.

Iterative Methods for Image Restoration

Although image restoration methods based on spectral filtering techniques are very efficient, they can be applied only to problems with fairly simple spatially invariant blurring operators. Iterative methods, however, are much more flexible; they can be very efficient for spatially invari- ant as well as spatially variant blurs, they can incorporate a variety of regularization techniques and boundary conditions, and they can more easily incorporate additional constraints, such as nonnegativity. This chapter describes a variety of iterative methods used in image restoration, with a particular emphasis on efficiency, convergence behavior, and implementation. Discussion of MATLAB software implementing the methods is also provided.

An Iterative L1L_{1}L1-Based Image Restoration Algorithm With an Adaptive Parameter Estimation

IEEE Transactions on Image Processing, 2012

Regularization methods for the solution of ill-posed inverse problems can be successfully applied if a right estimation of the regularization parameter is known. In this paper, we consider the 1-regularized image deblurring problem and evaluate its solution using the iterative forward-backward splitting method. Based on this approach, we propose a new adaptive rule for the estimation of the regularization parameter that, at each iteration, dynamically updates the parameter value, following the evolution of the objective functional. The iterative algorithm automatically stops, without requiring any assumption about the perturbation process, when the parameter has reached a seemingly near optimal value. In spite of the fact that the optimality of this value has not yet been theoretically proved, a large number of numerical experiments confirm that the proposed rule yields restoration results competitive with those of the best state-of-the-art algorithms.

Weight assignment for adaptive image restoration by neural networks

IEEE Transactions on Neural Networks, 2000

This paper presents a scheme for adaptively training the weights, in terms of varying the regularization parameter, in a neural network for the restoration of digital images. The flexibility of neural-network-based image restoration algorithms easily allow the variation of restoration parameters such as blur statistics and regularization value spatially and temporally within the image. This paper focuses on spatial variation of the regularization parameter. We first show that the previously proposed neural-network method based on gradient descent can only find suboptimal solutions, and then introduce a regional processing approach based on local statistics. A method is presented to vary the regularization parameter spatially. This method is applied to a number of images degraded by various levels of noise, and the results are examined. The method is also applied to an image degraded by spatially variant blur. In all cases, the proposed method provides visually satisfactory results in an efficient way.