Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Non-local Regularization of Inverse Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
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The widely adopted total variation (TV) filter is not optimal for MRI images with spatially varying noise levels, not to say those with also artifacts. To better preserve edges and fine structures while sufficiently removing noise and artifacts, we first use local mutual information together with k-means segmentation to automatically locate most of the reliable edges from the noisy input; noise and artifacts distribution at other regions are then studied using local variance; all obtained transparent information in turn guides fully automatic local adjustment of the TV filter. The proposed spatially adaptive TV model has been applied to partially parallel MRI (PP-MRI) image reconstructed using GRAPPA and SENSE. Comparison with Perona-Malik anisotropic diffusion and another adaptive TV verifies that the proposed model provides higher peak signal to noise ratio (PSNR) and results closer to ground truth. Numerical results on many in vivo clinical data sets demonstrate the robustness and viability of the unsupervised method.