Fast and robust multiframe super resolution.

Sina Farsiu; M Dirk Robinson; Michael Elad; Peyman Milanfar

doi:10.1109/tip.2004.834669

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Fast and robust multiframe super resolution.

Sina Farsiu·M Dirk Robinson·Michael Elad·Peyman Milanfar

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Abstract

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their short-comings. We propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.

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Algorithms Computer Graphics Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Numerical Analysis, Computer-Assisted Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Subtraction Technique Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Validation Study

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Sina Farsiu, M Dirk Robinson, Michael Elad, Peyman Milanfar. 2004. Fast and robust multiframe super resolution.. https://doi.org/10.1109/tip.2004.834669

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