It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K-singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low-resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal-to-noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high-resolution image. These parameters play an essential role in the reconstruction of the HR images.

中文翻译：

---

耦合字典学习算法的基于稀疏表示的CT层析图像重建

重建对临床医生分析疾病非常有用的高分辨率计算机断层扫描（CT）医学图像非常有趣。这项研究提出了一种改进的超分辨率方法，用于基于字典学习的稀疏表示域中的CT医学图像。稀疏耦合K奇异值分解（KSVD）算法用于字典学习。为了提高低分辨率图像的质量，图像分为低分辨率（LR）和高分辨率（HR）两组，作者使用KSVD算法编写了有关LR和HR图像块的字典。提出的方法的主要思想是，稀疏耦合字典学习每个补丁，并建立LR和HR图像补丁的稀疏系数之间的关系，以恢复LR图像的HR图像补丁。通过使用三个不同的数据集图像（包括CT胸部，CT牙科图像和CT脑图像），在平均峰值信噪比和结构相似性指标测量方面，将所提出的方法与常规算法进行了比较。作者还分析了针对不同字典大小和补丁大小而提出的改进方法，以获得相似的高分辨率图像。这些参数在HR图像的重建中起着至关重要的作用。