Single frame super-resolution reconstruction based on sparse representation

基于稀疏表示的单帧超分辨率重建（英文）

In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.

为了有效提高重建后的图像质量,提出了一种基于稀疏表示的单帧超分辨率重建方法.首先,该方法使用一种基于局部方向估计的图像块聚类和主元分析相结合的字典学习方法来获得一系列具有不同方向的几何字典.然后,给每一个待处理的图像块自动分配一个具有最近方向的字典,并据此进行稀疏编码.此外,为了在图像锐化和边缘保持方面取得进一步的提高,将梯度一致性加入提出的基本框架.在自然图像上进行的2组实验表明:提出的方法在视觉和数字指标方面均优于一些先进的同类方法.