Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Convex Optimization
EURASIP Journal on Advances in Signal Processing
Robust color image superresolution: an adaptive M-estimation framework
Journal on Image and Video Processing - Color in Image and Video Processing
Superresolution reconstruction using nonlinear gradient-based regularization
Multidimensional Systems and Signal Processing
A multi-frame image super-resolution method
Signal Processing
A super-resolution reconstruction algorithm for surveillance images
Signal Processing
Region-based weighted-norm with adaptive regularization for resolution enhancement
Digital Signal Processing
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Robust estimation approach for blind denoising
IEEE Transactions on Image Processing
An image super-resolution algorithm for different error levels per frame
IEEE Transactions on Image Processing
Super-Resolution Based on Fast Registration and Maximum a Posteriori Reconstruction
IEEE Transactions on Image Processing
A Robust and Computationally Efficient Simultaneous Super-Resolution Scheme for Image Sequences
IEEE Transactions on Circuits and Systems for Video Technology
Multiframe Super-Resolution Reconstruction Using Sparse Directional Regularization
IEEE Transactions on Circuits and Systems for Video Technology
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Multiframe image super-resolution is a technique to reconstruct a high-resolution image by fusing a sequence of low-resolution images of the same scene. In this paper, we propose a new multiframe image super-resolution algorithm built on the regularization framework. The objective functional to be minimized for the regularization framework consists of a fidelity term and a regularization term. A new adaptive norm combining the advantages of traditional L"1 and L"2 norms is used in both terms. The fidelity term is then formed by an adaptive strategy depending on the accuracies of the estimated low-resolution image observation models. This strategy serves to adaptively weight low-resolution images according to their reliability and can add robustness in practical implementation of super-resolution. The proposed regularization term can preserve sharp edges well without producing visual artifacts. Our experimental results using both synthetic and real data show the performance improvement of the proposed algorithm over other methods.