基于稀疏表示的单幅图像超分辨率重建研究-模式识别与智能系统专业论文.docxVIP

摘要 图像超分辨率重建技术作为一种无须改善硬件设备即可显著提高图像分辨率的技术，已 经成为图像处理领域的一个研究热点。最近，基于图像稀疏表示理论的图像超分辨率重建技 术通过构造一过完备字典对以及图像相对于该字典的稀疏表示系数，实现了较好的重建结果， 而且对噪声有较强的鲁棒性。为了进一步提高图像重建质量和改善字典学习效率，本文基于 图像稀疏表示理论，主要研究了基于新型字典学习方法的图像超分辨率重建，主要研究工作 如下： （1）在不降低字典表达能力的前提下，为了减少字典学习时的训练样本，从而提高字典 训练效率，基于图像的自相似性，提出了将待重建低分辨率图像作为训练时的高分辨率图像 的思想，而训练时对应的低分辨率图像由其经过模糊和下采样得到。 （2）在待重建图像作为训练图像的前提下，研究了一种基于 K-SVD 字典学习的图像超 分辨率重建方法。K-SVD 字典学习算法可以有效地减少训练时字典原子的个数，并且训练后 的原子仍然可以线性地表示初始字典的所有信息，从而有效地提高了利用过完备字典对待重 建图像进行稀疏编码时的效率。仿真实验证明，该算法确实改善了图像重建效率，而且还大 大提高了图像的重建质量。 （3）研究了一种新颖的 SCDL（Semi-Coupled Dictionary Learning,半耦合字典学习）方 法在图像超分辨率重建上的应用，该方法松驰了高低分辨率图像相对于过完备字典具有相同 的稀疏表示这一假设条件，通过对训练图像学习，能够同时得到一过完备字典对以及一个反 映图像稀疏表示之间真实关系的映射函数。仿真实验表明，运用该字典学习算法重建出的图 像质量明显优于传统的稀疏表示方法。 关键词: 稀疏表示，超分辨率重建 ，字典学习 ，稀疏编码 I I II Abstract As a technology which no need to improve hardware devices,but can significantly improve image resolution,the image super-resolution reconstruction has become a hot spot of research in the field of image processing.Recently,the reconstruction algorithms based on image sparse representation, which learning a pair of over-complete dictiontionaries from example images and a sparse representation coefficient of the test image over the low-resoluton dictionary,have achived better reconstruction results,and this kind of algorithms posses a strong robust to noise.In order to further improve the reconstruction’s quality and the efficiency of dictionary learning,this paper focuses on the survey of image super-resolution reconstruction based on the sparse representation and the new dictionary learning methods.The main works are as follows: In order to reduce the amounts of training samples, and thus improving the efficiency of dictionary learning without debasing the ability to representation of over-dictionary,a method of choose training images based image self-similarity is given.In this method,the single test image is treated as the high resolution training image,and the low resolution training image is down-sampled from a blurred versio