Image Restoration Scheme Research Articles

Improving Shape From Focus Using Defocus Cue

The shape-from-focus (SFF) method uses a sequence of frames to estimate the structure of a 3-D object. Its accuracy depends on the step size by which the translational table is moved while capturing the images. Existing SFF algorithms use an ad hoc interpolation strategy to account for the error due to the finite step size. We propose an improved SFF method that uses relative defocus blur derived from actual image data to arrive at the final estimates of the structure of the object. A space-variant image restoration scheme is also proposed to obtain a focused image of the 3-D object. The reconstructed 3-D structure as well as the quality of the restored image are superior for the proposed method in comparison to traditional SFF.

Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems

This paper presents a global approach for the automatic inspection of tiny pinhole defects in randomly textured surfaces of surface barrier layer (SBL) chips. By means of a discrete cosine transform (DCT)-based image restoration scheme, the proposed method is independent of textural features and thus not confined by the limitations of feature extraction based methods. Through properly decomposing the frequency matrix of an image in the DCT domain and selecting the best radius of the sector filter for the high-pass filtering operation, we effectively attenuate the global random texture pattern and accentuate only tiny pinhole defects in the restored image. We also develop two accumulative sum detection procedures that automatically determine the best high-pass filtering parameters based on the abrupt changes of the frequency coefficients in the decomposed matrix. Experimental results show that the proposed method outperforms the traditional approach in reducing the Type I error by 70–80% and in decreasing the deviation of the defect areas by 95%. Moreover, the proposed method can be applied to various types of passive components in large-batch production because no precise positioning of the target chip or template matching is required.

Image restoration using the particle filter: handling non-causality

A recursive-state estimation scheme for image restoration using the particle filter is described. Handling non-causal blurs within a recursive framework is a challenging problem. Most recursive image restoration schemes assume the blur to be causal or semi-causal in nature, but this is unrealistic. A novel choice for the state vector and a concurrent block estimation technique to incorporate full-plane regions of support for the image model as well as the blur are proposed. The particle filter-based framework enables general types of degradations to be tackled. The method assumes that the functional form of the distortion as well as the noise statistics are known but does not place any restrictions on them. Several experimental results are presented to validate the approach.

Constrained iterations for blind deconvolution and convexity issues

The need for image restoration arises in many applications of various scientific disciplines, such as medicine and astronomy and, in general, whenever an unknown image must be recovered from blurred and noisy data [M. Bertero, P. Boccacci, Introduction to Inverse Problems in Imaging, Institute of Physics Publishing, Philadelphia, PA, USA, 1998]. The algorithm studied in this work restores the image without the knowledge of the blur, using little a priori information and a blind inverse filter iteration. It represents a variation of the methods proposed in Kundur and Hatzinakos [A novel blind deconvolution scheme for image restoration using recursive filtering, IEEE Trans. Signal Process. 46(2) (1998) 375–390] and Ng et al. [Regularization of RIF blind image deconvolution, IEEE Trans. Image Process. 9(6) (2000) 1130–1134]. The problem of interest here is an inverse one, that cannot be solved by simple filtering since it is ill-posed. The imaging system is assumed to be linear and space-invariant: this allows a simplified relationship between unknown and observed images, described by a point spread function modeling the distortion. The blurring, though, makes the restoration ill-conditioned: regularization is therefore also needed, obtained by adding constraints to the formulation. The restoration is modeled as a constrained minimization: particular attention is given here to the analysis of the objective function and on establishing whether or not it is a convex function, whose minima can be located by classic optimization techniques and descent methods. Numerical examples are applied to simulated data and to real data derived from various applications. Comparison with the behavior of methods [D. Kundur, D. Hatzinakos, A novel blind deconvolution scheme for image restoration using recursive filtering, IEEE Trans. Signal Process. 46(2) (1998) 375–390] and [M. Ng, R.J. Plemmons, S. Qiao, Regularization of RIF Blind Image Deconvolution, IEEE Trans. Image Process. 9(6) (2000) 1130–1134] show the effectiveness of our variant.

FLANN Detector Based Filtering of Images Corrupted by Impulse Noise

We present a novel non-linear scheme for image restoration based on neuro-detector using Functional Link Artificial Neural Network (FLANN) followed by an improved spatial filter. The method is applied to images corrupted by impulse noise with varying strengths and different noise probability. The neural detector is based on the concept of training or learning by examples. When trained properly, the detector used to detect impulse noise in any image degraded by impulse noise. Hence, the method is suitable for real time image restoration applications. The simulated results obtained from the proposed scheme outperforms existing approaches are highly satisfactory and it outperforms the earlier suggested methods in terms of residual NSR in restored images.

Edge-preserving and scale-dependent properties of total variation regularization

We give and prove two new and fundamental properties of total-variation-minimizingfunction regularization (TV regularization): edge locations of function features tend to bepreserved, and under certain conditions are preserved exactly; intensity change experiencedby individual features is inversely proportional to the scale of each feature. We give andprove exact analytic solutions to the TV regularization problem for simple but importantcases. These can also be used to better understand the effects of TV regularization for moregeneral cases. Our results explain why and how TV-minimizing image restoration canremove noise while leaving relatively intact larger-scaled image features, and thus why TVimage restoration is especially effective in restoring images with larger-scaled features.Although TV regularization is a global problem, our results show that the effects of TVregularization on individual image features are often quite local. Our results give us abetter understanding of what types of images and what types of image degradationare most effectively improved by TV-minimizing image restoration schemes, andthey potentially lead to more intelligently designed TV-minimizing restorationschemes.

Automatic band selection for wavelet reconstruction in the application of defect detection

In this paper, we present a multiresolution approach for the inspection of local defects embedded in homogeneously textured surfaces. It is based on an efficient image restoration scheme using the wavelet transforms. By properly selecting the smooth subimage or the combination of detail subimages at different resolution levels for image reconstruction, the global repetitive texture pattern can be effectively removed and only local anomalies are preserved in the restored image. A wavelet band selection procedure is developed to automatically determine the best reconstruction parameters based on the energy distribution of wavelet coefficients. Experimental results show that the decomposed subimages and the number of resolution levels determined by the automatic band selection scheme are similar to the manual selection results, and the defects in a variety of real textures including machined surfaces, natural wood, sandpaper and textile fabrics are well detected.

Nonideal projection data in x‐ray computed tomography (in English)

This thesis deals with various aspects of x‐ray computed tomography (CT) from nonideal projection data that do not permit the successful application of standard image reconstruction methods. A versatile calibration method was implemented allowing an adequate correction for geometric flex effects introduced by nonideal geometry during data acquisition. A filtered backprojection algorithm was modified such that the geometric information from the calibration can be considered with high accuracy and in a computationally efficient way. The methods proposed here were evaluated by means of computer simulations and measurements using two experimental cone‐beam CT scanners. Results are presented that demonstrate their excellent performance. Furthermore, a new retrospective image restoration scheme was developed in order to tackle the problem of inconsistent projection data due to internal organ motion. This scheme was applied to simulated projections as well as clinical CT data of a beating heart. The results of these investigations indicate that the image restoration approach is a promising alternative to existing, well‐established image reconstruction methods if considerable organ motion occurs during data acquisition.

Solvable Markov random field model in color image restoration.

We propose a scheme for image restoration of full color images by means of a solvable probabilistic model in the red-green-blue space. A special case of our solvable probabilistic model is equivalent to a multicomponent Gaussian model in the statistical mechanics. Exact closed expressions of the evidence and the expectation value of intensity at each pixel in our solvable probabilistic model can be obtained by using multidimensional Gaussian integral formulas and a discrete Fourier transform. In the present paper, the degradation process is assumed to be an additive white Gaussian noise. Hyperparameters are determined so as to maximize the evidence that is expressed in terms of the partition function in our solvable probabilistic model. This work is a pioneering work for the Bayesian approach to the color image restoration by means of the statistical-mechanical technique.

Automatic surface inspection using wavelet reconstruction

In this paper, we present a multiresolution approach for the inspection of local defects embedded in homogeneous textured surfaces. The proposed method does not rely on the extraction of local textural features in a pixel-by-pixel basis. It is based on an efficient image restoration scheme using the wavelet transforms. By properly selecting the smooth subimage or the combination of detail subimages in different decomposition levels for backward wavelet transform, the reconstructed image will remove regular, repetitive texture patterns and enhance only local anomalies. Then, a simple thresholding can be used to discriminate between defective regions and homogeneous regions in the reconstructed image. This converts the difficult defect detection in complicated textured images into a simple binary thresholding in nontextured images. Effects of different wavelet bases, the number of multiresolution levels, the decomposed subimages used for reconstruction, and the change in image rotation on defect detection are thoroughly evaluated and discussed based on the experiment on a variety of real textures including machined surfaces, natural wood, textile fabrics, sandpaper and leather.

Improved blind image restoration scheme using recurrent filtering

In the paper, the idea of space-adaptive regularisation is introduced into the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image restoration. A newly developed cost function is obtained by adding a space-adaptive regularisation term to the cost function of the NAS-RIF algorithm to prevent noise amplification and to reduce ringing artifacts. Compared to the original NAS-RIF algorithm, the improved NAS-RIF algorithm is particularly effective under low SNR conditions. Also, the experimental results show that the co-vergence characteristic of the improved NAS-RIF algorithm is much more stable and the converged solution is capable of providing an excellent estimate of the original image.

Image restoration by regularisation in uncorrelated transform domain

Conventional spatially adaptive regularised image restoration schemes weight the amount of regularisation according to the spatial content of an image. The authors first separately decorrelate the signals under analysis into uncorrelated components and then weight the amount of regularisation performed to these components accordingly. The proposed approach works better than conventional schemes, especially in edge regions.

Automated surface inspection for directional textures

In this paper we present a global approach for the automatic inspection of defects in directionally textured surfaces which arise in textile fabrics and machined surfaces. The proposed method does not rely on local features of textures. It is based on a global image restoration scheme using the Fourier transform. The line patterns of any directional textures in the spatial domain image are removed by detecting the high-energy frequency components in the Fourier domain image using a one-dimensional (1D) Hough transform, setting them to zero, and finally back-transforming to a spatial domain image. In the restored image, the homogeneous line region in the original image will have an approximately uniform gray level, whereas the defective region will be distinctly preserved. A statistical process control scheme is therefore used to set up the control limits for discriminating between defects and homogeneous line patterns. The experiments on a variety of textile fabrics, machined surfaces and natural wood have shown the effectiveness of the proposed method.

A novel blind deconvolution scheme for image restoration using recursive filtering

We present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function. The technique applies to situations in which the scene consists of a finite support object against a uniformly black, grey, or white background. This occurs in certain types of astronomical imaging, medical imaging, and one-dimensional (1-D) gamma ray spectra processing, among others. The only information required are the nonnegativity of the true image and the support size of the original object. The restoration procedure involves recursive filtering of the blurred image to minimize a convex cost function. We prove convexity of the cost function, establish sufficient conditions to guarantee a unique solution, and examine the performance of the technique in the presence of noise. The new approach is experimentally shown to be more reliable and to have faster convergence than existing nonparametric finite support blind deconvolution methods. For situations in which the exact object support is unknown, we propose a novel support-finding algorithm.

Image restoration using cellular neural network

A 3D cellular neural network is applied to the restoration of noisy and blurry images. Contrary to similar solutions offered using a discrete-time Hopfield network, only eight cells are used for each pixel diminishing the network size. Moreover, the presented image restoration scheme does not have a convergence problem which is inherent in discrete-time Hopfield network solutions.

Lower bound on average mean-square error for image restoration

An average mean-square error bound that is applicable to general image observation models involving degradations of blur, signal-dependent and signal-independent noise, and sensor nonlinearity is derived. A Cramer-Rao lower bound on average mean-square errors for any unbiased image restoration scheme is derived. This bound is analytically expressed as a function of degradation parameters of imaging systems. Potential performance improvements by incorporating signal-dependent noise or sensor nonlinearity into algorithmic design are discussed. >

An iterative method for restoring noisy blurred images

This paper introduces a new iterative image restoration method which is capable of restoring noisy, blurred images by incorporating a priori knowledge about the image and noise statistics into the iterative procedure. The iteration equation consists of a prediction part which is based on a noncausal image model description and an innovation part which is weighted by a gain factor. The gain is computed using a linear MSE optimization procedure and is updated at each step of the iteration. This image restoration scheme can be interpreted as an iterative procedure with a statistical constraint on the image data.

On the numerical feasibility of digital image restoration

Computational aspects of the image restoration schemes described by Sondhi are discussed. It is shown that severe numerical difficulties limit their direct use in practical applications.