期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：3
语种：English
出版社：IAENG - International Association of Engineers
摘要：Sparse representation models have been widely used in single-image super-resolution reconstruction. The construction of dictionaries is especially important in these models. Basically, approaches of dictionary construction in sparse representation can be divided into two categories: analytical and learning-based approaches. Analytical approaches are effective and fast, but they are unable to fit different types of data; learning-based approaches are adaptive, but their implementation takes a significant amount of time. In this study, an image super-resolution reconstruction algorithm based on double-sparsity dictionaries is proposed. The algorithm combines the efficiency of analytical approaches and adaptability of learning-based approaches. In addition to the sparsity prior, the non-local self-similarity prior is also considered in the algorithm. Non-local means filtering is used to be the constraints on regularized super-resolution reconstruction procedures, which improves the quality of super-resolution reconstruction results further, while the runtime of the algorithm is still acceptable. Experimental results demonstrate the advantage of the proposed algorithm.
