【作者】 郭昌；

【导师】 高清维；

【作者基本信息】 安徽大学 ， 模式识别与智能系统， 2013， 硕士

【摘要】 随着图像采集传感器技术的发展,虽然已经可以获得较高分辨率的图像,但是由于硬件制作水平的限制,如何利用软件方法提高图像分辨率日益受到关注。传统的超分辨率插值算法由于实现原理的局限性,没有充分考虑图像数据中的空间梯度信息和统计特征,无法较好地识别图像边缘,从而导致边缘细节模糊或出现阶梯状锯齿现象。本文研究了一种基于小波变换与隐马尔可夫树(HMT)模型相结合的图像超分辨率插值方法。针对小波系数进行小波域HMT模型建模,使用混合高斯模型描述小波各子带系数的概率统计分布情况,利用树状Markov结构来反映小波系数在尺度间的相关性,对小波系数相对应的隐状态建模。为了实现小波系数与HMT模型匹配,采用期望值最大化(EM)算法来估计HMT模型参数,将图像超分辨率插值问题转化为一个约束优化问题。实验结果显示的效果图与实验数据,验证了本文算法的有效性和实用性。更多还原

【Abstract】 With the development of sensor technology of image acquisition, though the higher resolution image can be obtained, but the production level due to hardware limitations, and how to use the software methods to improve the image resolution is growing concern.The conventional super-resolution image interpolation algorithm due to the limitation of principle, not fully consider the space gradient information and the statistics characteristic of image data, it also can not effectively identify image edge, resulting in blurred edge details or stepped aliasing edge.This paper deals with the super-resolution image interpolation method based on wavelet transform combined with hidden Markov tree (HMT) model. Wavelet coefficients are modeled as the wavelet domain HMT model, using Gaussian mixture model to describe the probability statistical distribution of wavelet coefficients of each sub-band. Use of the tree shape Markov reflected in the structure of the wavelet coefficients across the scales, the wavelet coefficients corresponding to the hidden state modeling. In order to achieve the wavelet coefficients and the HMT model matching using the expectation maximization (EM) algorithm to estimate the HMT model parameters, the super-resolution image interpolation problem is transformed into a constrained optimization problem. The experimental results show that the effect of images with the experimental data, to verify the effectiveness and practicality of the proposed algorithm.更多还原