Abstract
In this paper, we propose an interpolation-based single-frame image super-resolution approach using orthogonal rotation invariant moments (ORIMs). The ORIMs have several useful characteristics in addition to having the property of image reconstruction. Therefore, they have been used successfully in many image processing applications. Among the various ORIMs, Zernike moments (ZMs), pseudo-Zernike moments (PZMs) and orthogonal Fourier-Mellin moments (OFMMs) have been considered in our proposed framework. The SR performance of the proposed approach has been compared with the classical interpolation-based approaches like bicubic, cubic B-spline, and Lanczos, as well as with nonlocal-means (NLM), and recently developed NLM+ZMs and NLM+PZMs-based SR approaches on twelve standard test images. The experiments have been conducted on both noise-free and noisy LR images corrupted with uniform blur and Gaussian noise. The results demonstrate the superiority of the proposed ORIMs-based approach in super-resolving both noise-free and noisy images over recently developed NLM+ORIMs-based SR approaches. Also, a comparative performance analysis, among various ORIMs (ZMs, PZMs, and OFMMs), is also presented to determine the ORIM which performs better over others under a given condition. A time complexity analysis shows that the proposed method is very fast as compared to NLM, NLM+ZMs and NLM+PZMs-based methods.
Published Version
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