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首页> 外文会议>Advances in Image and Video Technology; Lecture Notes in Computer Science; 4319 >Multi-scale MAP Estimation of High-Resolution Images
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Multi-scale MAP Estimation of High-Resolution Images

机译：高分辨率图像的多尺度MAP估计

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In this paper, a multi-scale MAP algorithm for image super-resolution is proposed. It is well known that Reconstructing high-resolution(HR) images from multiple low-resolution(LR) images or a single one is an ill-posed problem. The main challenge is how to preserve edges in images while reducing noise. According to Bayesian approaches, which are popular and widely researched, solving this kind of problems is introducing prior knowledge about HR images as constraints and obtaining good HR images in some sense. In this paper, wavelet-domain prior distributions are concisely analyzed. And then, by introducing wavelet-domain Hidden Markov Tree-structured model(HMT) which accurately characterizes the statistics of most real-world images, reconstruction of HR images is reformulated as a multi-scale MAP estimation problem. For justification of this formulation, HMT is interpreted in the regularization framework, concisely and clearly. Experimental results are presented for assessment.
机译：本文提出了一种用于图像超分辨率的多尺度MAP算法。众所周知，从多个低分辨率（LR）图像或单个图像重构高分辨率（HR）图像是不适的问题。主要的挑战是如何在减少噪声的同时保留图像的边缘。根据流行和广泛研究的贝叶斯方法，解决此类问题的方法是引入有关HR图像的先验知识作为约束，并在某种意义上获得良好的HR图像。本文对小波域先验分布进行了简要分析。然后，通过引入小波域隐马尔可夫树结构模型（HMT），该模型可以准确地描述大多数真实世界图像的统计数据，将HR图像的重构重新表述为多尺度MAP估计问题。为了证明这种表述的正确性，HMT在正则化框架中进行了简洁明了的解释。提出实验结果以供评估。

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