Texture Inpainting Using Efficient Gaussian Conditional Simulation

Texture Inpainting Using Efficient Gaussian

Inpainting consists in computing a plausible completion of missing parts of an image 4 given the available content. In the restricted framework of texture images, the image can be seen as a 5 realization of a random field model, which gives a stochastic formulation of image inpainting: on the 6 masked exemplar one estimates a random texture model which can then be conditionally sampled in 7 ord...

Inpainting using Multiresolution Gaussian Pyramid

Image inpainting is the technique of filling-in damaged regions by the surrounding information without affecting the contents and quality of the image i.e. restoration of an image using computers. Bertalmio proposed the first digital inpainting technique and coined the word ‘inpainting’ to the restoration work of digital images. Digital image inpainting is an interesting research topic and gath...

Efficient sampling of Gaussian graphical models using conditional Bayes factors

Bayesian estimation of Gaussian graphical models has proven to be challenging because the conjugate prior distribution on the Gaussian precision matrix, the G-Wishart distribution, has a doubly intractable partition function. Recent developments provide a direct way to sample from the G-Wishart distribution, which allows for more efficient algorithms for model selection than previously possible...

Application of Sequential Gaussian Conditional Simulation to Underground Mine Design Under Grade Uncertainty

In mining projects, all uncertainties associated with a project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation, as a reliable approach, is the most widely used method to quantify risk analysis to overcome the drawbacks of the estimation methods...

Efficient Gradient Computation for Conditional Gaussian Models