A blind superresolution method that works for arbitrary motions and point spread functions
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Video enhancement using a robust iterative SRR based on Leclerc stochastic estimation
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Joint image registration and super-resolution reconstruction based on regularized total least norm
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Vehicle license plate super-resolution using soft learning prior
Multimedia Tools and Applications
Coordinate-descent super-resolution and registration for parametric global motion models
Journal of Visual Communication and Image Representation
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
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This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.