Image restoration using quantum fluctuation

This paper presents a novel texture segmentation method using neural networks and a Markov random field (MRF) model. Multi-scale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Initially, the multi-scale texture segmentation is performed by the posterior probabilities from the neural networks and MAP (maximum a posterior) classification. Then the MAP segmentation maps are produced at all scales. In order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multi-scale MAP segmentations sequentially from coarse to fine scales. This is done by computing the MAP segmentation given the segmentation map at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale segmentation. In this fusion process, the MRF prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier.

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

We propose a deterministic algorithm for image restoration using a nonlinear Markov random field (MRF) model. Recent advances in measurement techniques allow us to obtain a large quantity of imaging data in various natural science fields. These data are often exposed to observation noise. For the removal of noise from imaging data, we use an MRF model, in which the Bayesian inference framework enables us to estimate hyperparameters through free-energy minimization. When a nonlinear function represents an observation process, a Markov chain Monte Carlo (MCMC) method is often used for image restoration. An MCMC method retains nonlinearity, but it is a probabilistic algorithm, which increases computational cost. The proposed deterministic algorithm linearizes the observation process to achieve more efficient hyperparameter estimation and image restoration. We also applied the proposed algorithm to artificial images to show its efficiency.

In this paper, a novel approach has been proposed to register noisy brain multimodal images. Specifically, we have considered CT and MR images. The MR image is the noisy one that has been considered to be the floating image while CT image is the fixed one. The joint histogram of the CT and noisy MR image has been modeled as Markov Random Field (MRF). Every element of the joint histogram has been estimated using the Maximum a Posterior (MAP) estimation criterion. The MAP estimates have been obtained jointly by Simulated Annealing (SA) and Iterative Conditioned Mode (ICM) algorithms. This estimated joint histogram has been used to compute the mutual information for different values of the registration parameter. The registration parameter has been estimated with 99 percent confidence interval. The proposed scheme has been successfully tested with different noise conditions and has been compared with five existing methods.

Noisy multimodal brain image registration using markov random field model

Image segmentation plays an important role in medical image processing. The aim of conventional hard segmentation methods is to assign a unique label to each voxel. However, due to the limited spatial resolution of medical imaging equipment and the complex anatomic structure of soft tissues, a single voxel in a medical image may be composed of several tissue types, which is called partial volume (PV) effect. Using the hard segmentation methods, the PV effect can substantially decrease the accuracy of quantitative measurements and the quality of visualizing different tissues. In this paper, instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing an image segmentation framework of maximum a posterior (MAP) probability. A new Markov random field (MRF) model was used to reflect the spatial information for the tissue mixture. Parameters of each tissue class were estimated through the expectation-maximization (EM) algorithm during the MAP tissue mixture segmentation. The MAP-EM mixture segmentation methodology was tested by digital phantom MR and patient CT images with PV effect evaluation. Results demonstrated that a hard segmentation method would lose a significant amount of details along the tissue boundaries, while the presented new PV segmentation method can dramatically improve the performance of preserving the details.

A new image segmentation algorithm based on Markov random field (MRF) and ant colony system (ACS) is presented in this paper. Information positive feedback and heuristic search, the characters of ACS, were applied for the image segmentations with MRF model. The maximum a posterior (MAP) global best solution of segmentations will be got though MRF, which describes image data relations by local correlations instead of global image possibility distributions. Compared with the simulated annealing (AS), ACS needs less time to search the global best solution. In this paper we proposed a segmentation algorithm combined MRF with ACS, which not only applied ACS as optimization algorithm but also introduced the neighborhood pheromone interaction rules into ACS under MRF model. Especially the pheromone interaction update provided remunerative information to ants in a neighborhood instead of an ant, which could accelerate the optimizing velocity and restrain the relative blur noise. The followed image segmentations experiments proved that this novel algorithm could reach a satisfied result among the noise restraint, edges preservation and computation complexity.

In order to overcome the poor anti-noise performance of traditional fuzzy C-Means (FCM) algorithm in image segmentation, a novel improved FCM algorithm was proposed in this paper based on Particle Swarm Optimization (PSO) algorithm and Markov Random Field (MRF) model, which can make full use of the global searching ability of PSO and the spatial information integrating ability of MRF for image segmentation. In this algorithm, the image segmentation is converted to a PSO optimization problem, in which the fitness function is set up to containing the spatial information based on the spectral value and the neighboring pixels modeled by MRFs. And segmentation results can be iteratively obtained during the PSO iterations according to the newly designed membership function of FCM in which the spatial information is integrated. The experiments herein reported in this paper illustrate the better performance of this algorithm than the traditional FCM algorithm and the PSO algorithm for image segmentation.

This paper presents an effective Bayesian wavelet shrinkage approach, in which a new Markov random field (MRF) model is used to improve the noise suppression performance. It is known that relatively few large wavelet coefficients carry the essential information of an image. These coefficients tend to cluster around the location of important feature in the image, such as edge discontinuities, peaks, and corners. This spatial clustering property is expressed in a prior model in the Bayesian framework. This paper proposes a novel prior model that models the configurations of the wavelet coefficients as a MRF. This model is adaptive to the wavelet transform character of two-dimension images that significant coefficients form clusters with predominantly the horizontal, vertical, or diagonal direction in each orientation subband. This prior model consequently effects the manipulation of the coefficients and a spatially adaptive Bayesian shrinkage function is obtained. Experimental results demonstrate this method can effectively suppress additive Gaussian white noise and preserve the details of the image. The performance surpasses related earlier methods quantitatively and qualitatively.

A valid multiscale classification method of synthetic aperture radar (SAR) imagery is proposed based on multiscale technology and Markov random field (MRF) mode. Firstly, we employ multiscale autoregressive model for extracting the feature of SAR image. which is modeled by Markov random field (MRF) Model that relies on the Gaussian distribution. Secondly, using the joint probability distribution in terms of an energy function, estimation of parameters can be performed by the stochastic relaxation algorithm. Then the maximum posteriori (MAP) is designed as the optimal criterion and the final labels are obtained by the simulated annealing algorithm. Experimental results show that this method is accurate, efficient and robust.

We present a new method using the Bayesian approach and a Markov random field (MRF) model of integrating several low level visual modules. Using this approach, we have shown how results from an edge based stereo module can be integrated with an intensity based stereo algorithm. In another example, results from a shape from shading module are combined with intensity based stereo. We first derive the intensity based stereo algorithm using the MRF model. The integration is then performed by coupling the results from other modules to the energy functional of the MRF associated with the intensity based stereo. The maximum a prosteriori (MAP) estimate of the resulting MRF is obtained using the mean field annealing algorithm. Results from real and artificial images show a consistent improvement in the accuracy after integration.

In this paper, we propose a sequence labeling method by using SVM posterior probabilities with a Markov Random Field (MRF) model for colorectal Narrow Band Imaging (NBI) zoom-videoendoscope. Classifying each frame of a video sequence by SVM classifiers independently leads to an output sequence which is unstable and hard to understand by endoscopists. To make it more stable and readable, we use an MRF model to label the sequence of posterior probabilities. In addition, we introduce class asymmetry for the NBI images in order to keep and enhance frames where there is a possibility that cancers might have been detected. Experimental results with NBI video sequences demonstrate that the proposed MRF model with class asymmetry performs much better than a model without asymmetry.

Based on the computer texture analysis method, a new noncontact surface roughness measurement technique is proposed. The method is inspired by the nonredundant directional selectivity and highly discriminative nature of the wavelet representation and the capability of the Markov random field (MRF) model to capture statistical regularities. Surface roughness information contained in the texture features may be extracted based on an MRF stochastic model of textures in the wavelet feature domain. The model captures significant intrascale and interscale statistical dependencies between wavelet coefficients. To investigate the relationship between the texture features and surface roughness Ra, a simple research setup, which consists of a charge-coupled diode camera without a lens and a diode laser, was established, and the laser speckle texture patterns are acquired from some standard grinding surfaces. The research results have illustrated that surface roughness Ra has a good monotonic relationship with the texture features of the laser speckle pattern. If this measuring system is calibrated with the surface standard samples roughness beforehand, the surface roughness actual value Ra can be deduced in the case of the same material surfaces ground at the same manufacture conditions.

We present a statistically-based method for the enhancement and detection of mines and mine-like targets, in multi-channel imagery. Standard approaches to such multi-channel image processing take advantage of the correlation across channels within a pixel, but typically do not exploit the spatial dependency between pixels. This work aims to construct appropriate spatial statistical models for multi-channel mine imagery and apply these models to allow both image enhancement as well as direct and improved detection of anomalies (i.e., targets) in such data. We base the method on a Markov Random Field (MRF) model that incorporates a priori information about both the target's and the background's spatial characteristics. In particular, we find a Maximum A Posterior (MAP) detector of mine targets in background under the prior assumption target pixels are locally spatially dependent. We implement our algorithm on polarimetric and thermal data obtained from the Remote Minefield Detection System (REMIDS), with favorable results compared to a Maximum Likelihood (ML) detector that performs detections on a pixel-by-pixel basis, i.e. without spatial correlation.

Image segmentation method combined with Markov random field (MRF) model and regional growth image in this paper is proposed to achieve the accuracy of sonar image segmentation. First of all, the characteristics of field and the label field of sonar image is established through the MRF model; secondly, the further image segmentation is processed by region growing based on the initial image segmentation by ICM algorithm, and this reduced segmentation plaques caused by the MRF method on image segmentation. Finally, experimental results show that the method in this paper can obtain the image segmentation effectively.