Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
High resolution image formation from low resolution frames using Delaunay triangulation
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Noniterative Interpolation-Based Super-Resolution Minimizing Aliasing in the Reconstructed Image
IEEE Transactions on Image Processing
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Super-resolution (SR) techniques produce a high resolution (HR) image from a set of low-resolution (LR) undersampled images. Usually, SR problems are posed as estimation problems where the LR images are contaminated by stationary noise. However, in real SR problems is very common to have non-stationary noise due to problems in the registration of the images or outliers. SR methods that address this type of problems are called robust. In this paper we propose a novel robust SR method that employs a median filter directly in the data from the LR images, before proceeding to the interpolation and deblurring steps that are common in SR. We compare this new method with other robust SR methods with synthetic and real data, proving that it outperforms the other methods in both cases.