Generalized Adaptive Gaussian Markov Random Field for X-Ray Luminescence Computed Tomography

Conclusion: Compared to conventional L2 and L1 regularizations, GAGMRF provides a new and efficient model for high quality imaging based on the Bayesian framework. Significance: The GAGMRF method offers a flexible regularization framework to adapt to a wide range of biomedical applications.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research