title: [Adaptive super resolution algorithm for under-sampled images].
abstract: A new algorithm of adaptive super-resolution (SR) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution (HR) image from the low-resolution (LR) image sequence, which takes into full account the inaccurate estimates of motion error, point spread function (PSF) and the additive Gaussian noise in the LR image sequence. We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size. This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence, which is verified by the experiment on optical images.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Peng
authors.forename: Qi-fei
authors.initials: QF
authors.lastname: Xu
authors.forename: Qing-wen
authors.initials: QW
authors.lastname: Lv
authors.forename: Zhi-yuan
authors.initials: ZY
authors.lastname: Wang
authors.forename: Yan-qiu
authors.initials: YQ
authors.lastname: Feng
authors.forename: Wu-fan
authors.initials: WF
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009038:Motion; D013997:Time Factors