While Markov random field (MRF) models have been widely used in the solution of inverse problems, a major disadvantage of these models is the difficulty of parameter estimation. At its root, this parameter estimation problem stems from the inability to explicitly express the joint distribution of an MRF in terms of the conditional distributions of elements given their neighbors. The objective of this paper is to provide a general approach to solving maximum a posteriori (MAP) inverse problems through the implicit specification of a MRF prior. In this method, the MRF prior is implemented through a series of quadratic surrogate function approximations to the MRF's log prior distribution. The advantage of this approach is that these surrogate functions can be explicitly computed from the conditional probabilities of the MRF, while the explicit Gibbs distribution can not. Therefore, the Gibbs distribution remains only implicitly defined. In practice, this approach allows for more accurate modeling of data through the direct estimation of the MRF's conditional probabilities. We illustrate the application of our method with a simple experiments of image denoising and show that it produces superior results to some widely used MRF prior models.

The mixed raster content (MRC) standard (ITU-T T.44) specifies a framework for document compression which can dramatically improve the compression/quality tradeoff as compared to traditional lossy image compression algorithms. The key to MRC compression is the separation of the document into foreground and background layers, represented as a binary mask. Therefore, the resulting quality and compression ratio of a MRC document encoder is highly dependent upon the segmentation algorithm used to compute the binary mask. In this paper, we propose a novel multiscale segmentation scheme for MRC document encoding based upon the sequential application of two algorithms. The first algorithm, cost optimized segmentation (COS), is a blockwise segmentation algorithm formulated in a global cost optimization framework. The second algorithm, connected component classification (CCC), refines the initial segmentation by classifying feature vectors of connected components using an Markov random field (MRF) model. The combined COS/CCC segmentation algorithms are then incorporated into a multiscale framework in order to improve the segmentation accuracy of text with varying size. In comparisons to state-of-the-art commercial MRC products and selected segmentation algorithms in the literature, we show that the new algorithm achieves greater accuracy of text detection but with a lower false detection rate of nontext features. We also demonstrate that the proposed segmentation algorithm can improve the quality of decoded documents while simultaneously lowering the bit rate.

The Mixed Raster Content (MRC) standard (ITU-T T.44) specifies a framework for document compression which can dramatically improve the compression/quality tradeoff as compared to traditional lossy image compression algorithms. The key to MRC's performance is the separation of the document into foreground and background layers, represented as a binary mask. In this paper, we propose a novel multiscale segmentation scheme based on the sequential application of two algorithms. The first algorithm, Cost Optimized Segmentation (COS), is a blockwise segmentation algorithm formulated in a global cost optimization framework. The second algorithm, Connected Component Classification (CCC), refines the initial segmentation by classifying feature vectors of connected components using a Markov random field (MRF) model. The combined COS/CCC segmentation algorithms are then incorporated into a multiscale framework in order to improve the segmentation accuracy of text with varying size.