Zernike-Moment-Based Image Super Resolution

Abstract

Multiframe super-resolution (SR) reconstruction aims to produce a high-resolution (HR) image using a set of low-resolution (LR) images. In the process of reconstruction, fuzzy registration usually plays a critical role. It mainly focuses on the correlation between pixels of the candidate and the reference images to reconstruct each pixel by averaging all its neighboring pixels. Therefore, the fuzzy-registration-based SR performs well and has been widely applied in practice. However, if some objects appear or disappear among LR images or different angle rotations exist among them, the correlation between corresponding pixels becomes weak. Thus, it will be difficult to use LR images effectively in the process of SR reconstruction. Moreover, if the LR images are noised, the reconstruction quality will be affected seriously. To address or at least reduce these problems, this paper presents a novel SR method based on the Zernike moment, to make the most of possible details in each LR image for high-quality SR reconstruction. Experimental results show that the proposed method outperforms existing methods in terms of robustness and visual effects.

Learning-Based Video Superresolution Reconstruction Using Spatiotemporal Nonlocal Similarity

“…This section describes the experiments that were carried out to evaluate the performance of the proposed LBST-SR superresolution reconstruction algorithm and a comparison of these results with five recently proposed representative state-of-the-art superresolution algorithms in terms of both visual quality and objective quantitative indices, including the learningbased ANRSR [22], DPSR [30], and ScSR [27] algorithms, the 3D nonlocal mean-based NL-SR [14] algorithm, and the Zernike moment-based ZM-SR [16] algorithm. In the experiments, ten benchmark and two spatial video sequences were used: "Forman," "Calendar," "Coastguard," "Suzie," "Mother Daughter," "Miss America," "Ice," "Football," "Carphone," "Akiyo," "Satellite-1," and "Satellite-2."…”

“…Using this novel scheme, Protter et al [14] proposed a nonlocal fuzzy registration scheme-based SR reconstruction framework based on a 3D nonlocal mean filter (3D NLM) [15]. Subsequently, Gao et al [16] improved the nonlocal similarity matching method based on Zernike moment feature similarity and proposed a novel Zernike moment-based SR method which improved the noise robustness and rotation invariance of the NLM-based SR process. However, multiframe-based SR methods cannot be adapted to a larger magnification factor and usually fail when insufficient complementary and redundant information between video frames is provided.…”

Super-resolution: a comprehensive survey

“…- [318], [426], [460], [464], [468] they are defined based on steering kernel regression which takes into account the correlation between the pixel positions and their values. - [518] they are found using Zernike moments.…”

Image analysis by circularly semi-orthogonal moments

“…(17) and (18) imply that the magnitude of the circularly semi-orthogonal moment is invariant to image flipping.…”

“…The Cartesian orthogonal moments such as Legendre moment [10], discrete Tchebichef moment [11], Krawtchouk moment [12], dual Hahn moment [13] and Racah moment [14] have been defined in the Cartesian coordinates, where moment invariants particularly rotation invariants are not readily available. The circularly orthogonal moments including Zernike moment (ZM) [15] and orthogonal Fourier-Mellin moment (OFM) [16] are defined in the polar coordinates, their magnitudes are natively rotation invariant, and so have been widely used in many image processing, pattern recognition and computer vision applications [17][18][19][20][21][22]. In our early work [23], a kind of circularly orthogonal moment namely Bessel-Fourier moment (BFM) was proposed, and has been used in image reconstruction and recognition [24].…”