A Bayesian image processing model is proposed based on, a Markovian multinomial prior. The technique has application in texture segmentation where its introduction of spatial context can improve segmentation accuracy by 60%. Other applications include general image restoration where 18 dB SNR improvement is possible. In addition, the computational complexity of the system is low, making it ideal as a component part of other systems. We show quantitative experiments to illustrate the performance of the algorithm, and groundtruth examples are provided to show the effect in practice.
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机译：提出了一种基于马尔可夫多项式先验的贝叶斯图像处理模型。该技术已应用于纹理分割，其中引入空间上下文可以将分割精度提高60％。其他应用包括可以提高18 dB SNR的常规图像恢复。此外，该系统的计算复杂度低，因此非常适合作为其他系统的组成部分。我们展示了量化实验来说明该算法的性能，并提供了实例说明了该算法在实践中的效果。
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