Adaptive Image Restoration Research Articles

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

A Review of Adaptive Image Representations

Improving the modeling of natural images is important to go beyond the state-of-the-art for many image processing tasks such as compression, denoising, inverse problems, and texture synthesis. Natural images are composed of intricate patterns such as regular areas, edges, junctions, oriented oscillations, and textures. Processing efficiently such a wide range of regularities requires methods that are adaptive to the geometry of the image. This adaptivity can be achieved using sparse representations in a redundant dictionary. The geometric adaptivity is important to search for efficient representations in a structured dictionary. Another way to capture this geometry is through non-local interactions between patches in the image. The resulting non-local energies can be used to perform an adaptive image restoration. This paper reviews these emerging technics and shows the interplay between sparse and non-local regularizations.

Iterative adaptive hybrid image restoration for fast convergence

We present an iterative adaptive hybrid image restoration algorithm for fast convergence. The local variance, mean, and maximum values are used to constrain the solution space. These parameters are computed at each iteration step using a partially restored image at each iteration, and they are used to impose the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed and better performance than typical regularized constrained least-squares approach.

Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzykriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.

A Gaussian Mixture Model-based regularization method in adaptive image restoration

A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth, edge or detail texture region according to variance-sum criteria function of the feature vectors. Then parameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration, and network weight value matrix is updated by the output of GMM. Since GMM is used, the regularization parameters share properties of different kind of regions. In addition, the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system, and it has strong generalization capability. Comparing with non-adaptive and some adaptive image restoration algorithms, experimental results show that the proposed algorithm obtains more preferable restored images.

High-Resolution Observations of Fast Events in the Solar Chromosphere

We present new, high spatial and high temporal resolution observations of the Sun in Hα obtained with the Swedish 1 m Solar Telescope on La Palma. The combined use of adaptive optics and image restoration techniques yielded a near-diffraction-limited time series with a cadence of 3 frames s-1 of two different active regions. The unique combination of high temporal and spatial resolution reveals to us the existence of highly dynamic structures, moving at velocities of up to 240 km s-1, and high-velocity waves in the chromosphere. The rapid motions appear to be common, as they are observed in two data sets recorded in succession at different locations on the solar disk. The dynamic events are probably associated with reconfigurations of the magnetic field.

Incorporating local statistics in image error measurement for adaptive image restoration

This paper presents an image restoration technique incorporating local statistical knowledge in the cost function. Instead of using a conventional grayscale-based error measurement such as the mean squared error, we compare local statistical information about regions in two images using a new error measure. Transient features such as edges and textures are more strongly emphasized than relatively homogeneous regions. With the addition of this local information, we attempt to provide a measure closer to human visual appraisal. We then extend the popular constrained squared-error cost function by incorporating this image error measure. Due to its nonlinear nature, conventional restoration algorithms cannot optimize this cost function efficiently. Therefore we seek an iterative approach. In particular, an extended neural network algorithm is proposed to perform the restoration. It is shown that this technique is efficient, effective, and robust. It compares favorably with other techniques when applied to both grayscale and color images. The results of a subjective survey comparing the proposed algorithm with a more conventional neural network algorithm are presented. The subjects tested in the survey overwhelmingly favored the results provided by the proposed method.

Adaptive GIS Image Compression and Restoration Using Neural Networks

This study aims to describe research into the field of GIS image compression, decompression and restoration. Geographical Information System (GIS) data comprises huge size into memory. For this purpose, it needs compression, which has high compression rate. But high compression rate cause of some distortion and losses. Restoration is a process by which an image suffering some form of distortion or degradation can be recovered to its original form. The proposed windows-based image compression and restoration was implemented using Delphi.

A neural learning approach for adaptive image restoration using a fuzzy model-based network architecture

We address the problem of adaptive regularization in image restoration by adopting a neural-network learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training examples. The desired response of the network is in the form of a gray level value estimate of the current pixel using weighted order statistic (WOS) filter. However, instead of replacing the previous value with this estimate, this is used to modify the network weights, or equivalently, the regularization parameters such that the restored gray level value produced by the network is closer to this desired response. In this way, the single WOS estimation scheme can allow appropriate parameter values to emerge under different noise conditions, rather than requiring their explicit selection in each occasion. In addition, we also consider the separate regularization of edges and textures due to their different noise masking capabilities. This in turn requires discriminating between these two feature types. Due to the inability of conventional local variance measures to distinguish these two high variance features, we propose the new edge-texture characterization (ETC) measure which performs this discrimination based on a scalar value only. This is then incorporated into a fuzzified form of the previous neural network which determines the degree of membership of each high variance pixel in two fuzzy sets, the EDGE and TEXTURE fuzzy sets, from the local ETC value, and then evaluates the appropriate regularization parameter by appropriately combining these two membership function values.

Adaptive image restoration based on hierarchical neural networks

We propose a new approach to adaptive image regularization based on a neural network, hierarchical cluster model (HCM). The HCM bears a close resemblance to image formation. Its sparse synaptic connections are effective in reducing the computational cost of restoration. We attempt to achieve adaptive restoration by assigning entries of a novel, optimized regularization vector to each homogeneous image region. The degraded image is first segmented and partitioned into smooth, texture, and edge clusters. It is then mapped onto a three-level HCM structure. An evolutionary scheme is proposed to optimize the regularization vector by minimizing the HCM energy function. The scheme progressively selects the well-evolved individuals that consist of partially restored images, their corresponding cluster structures, segmentation maps, and the optimized regularization vector. Experimental results show that the new approach is superior in suppressing noise and ringing at the smooth background while effectively preserving the fine details at the texture and edge regions. An important feature of the method is that the empirical relationship between the optimized regularization vector and the local perception measure can be reused in the restoration of other degraded images. This generalization removes the overhead of evolutionary optimization, thus rendering a very fast restoration.

Blocking effect reduction of compressed images using classification-based constrained optimization

Abstract In this paper we propose an adaptive image restoration algorithm using block-based edge-classification for reducing block artifacts in compressed images. In order to efficiently reduce block artifacts, edge direction of each block is classified by using model-fitting criterion, and the constrained least-squares (CLS) filter with corresponding direction is used for restoring the block. The proposed restoration filter is derived based on the observation that the quantization operation in a series of coding processes is a nonlinear and many-to-one mapping operator. Then we propose an approximated version of a constrained optimization technique as a restoration process for removing the nonlinear and space-varying degradation operator. For real-time implementation, the proposed restoration filter can be realized in the form of a truncated FIR filter, which is suitable for postprocessing reconstructed images in digital TV, video conferencing systems, etc.

Adaptive Image Reconstruction Using Information Measures

We present a class of nonlinear adaptive image restoration filters which may be steered to preserve sharp edges and contrasts in the restorations. From a theoretical point of view we discuss the associated variational problems and prove existence of solutions in certain Sobolev spaces W1,p or in a BV-space. The degree of regularity of the solution may be understood as a mathematical explanation of the heuristic properties of the designed filters.

Weight assignment for adaptive image restoration by neural networks

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.

Fast Image Restoration for Reducing Block Artifacts Based on Adaptive Constrained Optimization

In this paper we propose a fast adaptive image restoration filter using DCT-based edge-classification for reducing block artifacts in compressed images. In order to efficiently reduce block artifacts, edge direction of each block is classified by using the DCT coefficients, and an adaptive constrained least squares (CLS) filter along the edge direction is used for filtering the corresponding block. The proposed research is based on the observation that degradation due to block artifacts is a nonlinear and many-to-one mapping operator. Then, we propose an approximated version of a constrained optimization technique as a restoration process for removing the nonlinear and space-varying degradation operator. For real-time application, the proposed restoration filter is implemented in the form of a truncated FIR filter, which is suitable for postprocessing the images in real-time video systems such as HDTV or video conferencing systems.

Improved 2D joint process lattice for adaptive restoration of images

An improved method to obtain the 2D joint process lattice structure using recently developed 2D orthogonal quarter-plane filters for AR modelling is reported. It is shown that a set of orthogonal backward prediction error fields can be used to calculate the corresponding joint process coefficients.

A fast adaptive image restoration filter for reducing block artifact in compressed images

We propose a fast adaptive image restoration filter using DCT-based block-classification for reducing block artifact in compressed images. More specifically, edge direction of each block is classified by using the DCT coefficients, and the adaptive constrained least squares (CLS) filter with corresponding direction is used for smoothing the discontinuity between blocks. For real-time application, the proposed restoration filter can be realized in the form of a truncated FIR filter, which is suitable for postprocessing reconstructed images in HDTV, DVD, or video conferencing systems.

Improving optical character recognition accuracy using adaptive image restoration

A technique that can improve the optical character rec- ognition (OCR) accuracy of text images is presented. By using the output from an OCR system and a distorted text image, this tech- nique trains an adaptive restoration filter and then applies the filter to the distorted text that the OCR system could not recognize. The restored text image is then reprocessed by the OCR system, and the restored characters are recognized with a higher accuracy than the distorted text. A series of experiments were performed to deter- mine a feasible adaptive restoration filter architecture, to establish this technique's ability to restore uniformly distorted text, and to demonstrate this technique's ability to improve the OCR accuracy of real world text documents. The results of these experiments show that this technique can improve both pixel and OCR accuracy of distorted text images. © 1996 SPIE and IS&T.

Adaptive restoration of textured images with mixed spectra

We consider the adaptive restoration of inhomogeneous textured images, where the individual regions are modeled using a Wold-like decomposition. A generalized Wiener filter is developed to accommodate mixed spectra, and unsupervised restoration is achieved by using the expectation-maximization (EM) algorithm to estimate the degradation parameters. This algorithm yields superior results when compared with supervised Wiener filtering using autoregressive (AR) image models.

Spatially adaptive wavelet-based multiscale image restoration

In this paper, we present a new spatially adaptive approach to the restoration of noisy blurred images, which is particularly effective at producing sharp deconvolution while suppressing the noise in the flat regions of an image. This is accomplished through a multiscale Kalman smoothing filter applied to a prefiltered observed image in the discrete, separable, 2-D wavelet domain. The prefiltering step involves constrained least-squares filtering based on optimal choices for the regularization parameter. This leads to a reduction in the support of the required state vectors of the multiscale restoration filter in the wavelet domain and improvement in the computational efficiency of the multiscale filter. The proposed method has the benefit that the majority of the regularization, or noise suppression, of the restoration is accomplished by the efficient multiscale filtering of wavelet detail coefficients ordered on quadtrees. Not only does this lead to potential parallel implementation schemes, but it permits adaptivity to the local edge information in the image. In particular, this method changes filter parameters depending on scale, local signal-to-noise ratio (SNR), and orientation. Because the wavelet detail coefficients are a manifestation of the multiscale edge information in an image, this algorithm may be viewed as an "edge-adaptive" multiscale restoration approach.

Image restoration using a row action projection method with adaptive smoothing

We present a spatially and temporally adaptive iterative image restoration algorithm. It extends an earlier technique using row action projection (RAP) by continuously varying spatial adaptation for applying updates and smoothing instead of the earlier segmentation based approach. The new algorithm is a subset of the method of projection onto convex sets and incorporates any <i>a priori</i> information about the blurred image or the blur in order to reduce ambiguity in the solution and accelerate the speed of convergence. Our algorithm generalizes the projection concept to column action projection (CAP). In RAP, an error in the estimate of a pixel of the blurred image is used to update all those pixels of the restored image that contribute to it. In CAP, a pixel of the restored image is updated using the errors in the estimates of all those pixels of the blurred image that receive contributions from that pixel. This algorithm adds a way of emphasizing high-SNR regions during restoration, reducing edge blurs by using a new edge-oriented smoothing, morphologically processing the local variance of the restored image, and temporally varying the restoration parameters to provide significantly better SNR improvements during restoration.