Learning real-time MRF inference for image denoising

Computer Vision and Image Understanding, 2013

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.

2008

Abstract In this paper we propose novel algorithms for image restoration and parameter estimation with a generalized Gaussian Markov random field (GGMRF) prior utilizing variational distribution approximation. The restored image and the unknown hyperparameters for both the image prior and the image degradation noise are simultaneously estimated within a hierarchical Bayesian framework.

2013 IEEE International Conference on Image Processing, 2013

This article presents a new method for estimating the latent noiseless version of an observed image corrupted by additive noise. This method stems from classical models in parametric denoising and extends them by modeling the likelihood term, estimating adaptive image priors and automatically choosing an adaptive equivalent to the typically hand-tuned regularization constant. The proposed method introduces a possible path to overcome the limitations of current parametric denoising algorithms and provides a competitive alternative to powerful non-parametric ones. The experimental results show how our method adapts better to different noise types than state-of-the-art parametric and non-parametric algorithms.

Removing noise from original image is still a challenging problem for researchers. There have been several published algorithm and each approach has its assumptions, advantages and disadvantages. Markov Random Field is n-dimensional random process defined on a on a discrete lattice. Markov Random Field is a new branch of probability theory that promises to be important both in theory and application of probability. This paper is an attempt to present the basic idea of the subject and its application in image denoising to the wider audience. In this paper, a novel approach for image denoising is introduced using ICM (Iterated Conditional Modes) approach of Markov Random Fields model.

IEEE Transactions on Image Processing, 1999

Recent developments in statistics now allow maximum likelihood estimators for the parameters of Markov Random Fields to be constructed. We detail the theory required, and present an algorithm which is easily implemented and practical in terms of computation time. We demonstrate this algorithm on three MRF models the standard Potts model, an inhomogeneous variation of the Potts model, and a long-range interaction model, better adapted to modeling real-world images. We estimate the parameters from a synthetic and a real image, and then resynthesise the models to demonstrate which features of the image have been captured by the model. Segmentations are computed based on the estimated parameters and conclusions drawn.

The present work introduces an alternative method to deal with digital image restoration into a Bayesian framework, particularly, the use of a new halfquadratic function is proposed. The Bayesian methodology is based on the prior knowledge of some information that allows an efficient modelling of the image acquisition process. The edge preservation of objects into the image while smoothing noise is necessary in an adequate model. Thus, we use a convexity criteria given by a semi-Huber function to obtain adequate weighting of the cost functions (halfquadratic) to be minimized. A comparison between the new introduced scheme and other three existent schemes, for the cases of noise filtering and image deblurring, is presented. Results showed a satisfactory performance and the effectiveness of the proposed estimator. 1

SIAM Journal on Imaging Sciences

Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging and microscopy. State-of-the-art approaches are clearly dominated by patch-based non-local methods in recent years. In this paper, we aim to propose a local Poisson denoising model with both structure simplicity and good performance. To this end, we consider a variational modeling to integrate the so-called Fields of Experts (FoE) image prior, that has proven an effective higher-order Markov Random Fields (MRF) model for many classic image restoration problems. We exploit several feasible variational variants for this task. We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR. However, this strategy encounters problem in cases of low SNR. Then we turn to an alternative modeling strategy by using the Anscombe transform and Gaussian statistics derived data term. We retrain the FoE prior model directly in the transform domain. With the newly trained FoE model, we end up with a local variational model providing strongly competitive results against state-of-the-art non-local approaches, meanwhile bearing the property of simple structure. Furthermore, our proposed model comes along with an additional advantage, that the inference is very efficient as it is well-suited for parallel computation on GPUs. For images of size 512 × 512, our GPU implementation takes less

2011

Abstract A novel stochastic approach based on Markov-chain Monte Carlo sampling is investigated for the purpose of image denoising. The additive image denoising problem is formulated as a Bayesian least squares problem, where the goal is to estimate the denoised image given the noisy image as the measurement and an estimated posterior. The posterior is estimated using a nonparametric importance-weighted Markov-chain Monte Carlo sampling approach based on an adaptive Geman–McClure objective function.

Journal of Electronic Imaging, 2016

Currently, the state-of-the art methods for image denoising are patch-based approaches. Redundant information present in nonlocal regions (patches) of the image is considered for better image modeling, resulting in an improved quality of filtering. In this respect, nonlocal Markov random field (MRF) models are proposed by redefining the energy functions of classical MRF models to adopt a nonlocal approach. With the new energy functions, the pairwise pixel interaction is weighted according to the similarities between the patches corresponding to each pair. Also, a maximum pseudolikelihood estimation of the spatial dependency parameter (β) for these models is presented here. For evaluating this proposal, these models are used as an a priori model in a maximum a posteriori estimation to denoise additive white Gaussian noise in images. Finally, results display a notable improvement in both quantitative and qualitative terms in comparison with the local MRFs.

IEEE Signal Processing Letters, 2014

The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems. Motivated by the successes of FoE-based approaches, in this letter we propose a novel variational model for multiplicative noise reduction based on the FoE image prior model. The resulting model corresponds to a non-convex minimization problem, which can be efficiently solved by a recently published non-convex optimization algorithm. Experimental results based on synthetic speckle noise and real synthetic aperture radar (SAR) images suggest that the performance of our proposed method is on par with the best published despeckling algorithm. Besides, our proposed model comes along with an additional advantage, that the inference is extremely efficient. Our GPU based implementation takes less than 1s to produce state-of-theart despeckling performance.