期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 27, 期 8, 页码 3782-3797出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2018.2826139
关键词
Image super-resolution; rational fractal interpolation; image features; scaling factor; local fractal analysis
资金
- National Natural Science Foundation of China [61373080, 61672018, 61402261, U1609218]
- Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions
This paper presents a novel single-image superresolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.
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