Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Super-Resolution Enhancement of Text Image Sequences
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
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In recent years super-resolution (S-R) methods are starting to emerge in the field of medical imaging for the reconstruction of isotropic images with increased slice resolution. Use of the maximal likelihood S-R estimator is not advisable as the S-R reconstruction is an ill-posed problem. Regularizing the S-R algorithm using specific apriori knowledge may compensate for missing measurement information and improve the resolved result. In this work two novel regularization methods are proposed, utilizing domain-specific spatial and intensity constraints on brain MRI data. Experiments indicate that the proposed methods eliminate disadvantages of common regularization methods and outperform these methods with better edge definition and increased image quality.