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27 October 2013 An improved image super-resolution algorithm
Kai Xie, Xing Huo
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Proceedings Volume 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing; 89200A (2013) https://doi.org/10.1117/12.2030307
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Now many image super-resolution methods suppose that the optical flows between images should be computed accurately. But really it is very difficult to get them and the models of imaging systems are unknown almost. Thurs perturbation errors always occur in the image super-resolution model. The paper proposes an improved image super-resolution algorithm based on total least squares method. The average image based on images is used as regularized penalty for posteriori probability model. The paper presents the improved Rayleigh quotient format for energy objective function. Then a conjugate gradient algorithm is used to minimize the modified Rayleigh quotient function. The method can minimize two the errors from the sampled low-resolution images and in that perturbation system matrix of high-resolution reconstruction. The test results showed that the algorithm is stable for the perturbation system matrix.
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Kai Xie and Xing Huo "An improved image super-resolution algorithm", Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200A (27 October 2013); https://doi.org/10.1117/12.2030307
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KEYWORDS
Super resolution
Reconstruction algorithms
Lawrencium
Signal to noise ratio
Systems modeling
Image restoration
Imaging systems
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