Our proposed method builds on a Bayesian estimate of heteroscedastic aleatoric uncertainty of a region of white matter by inpainting it from its context.
To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both continuous and discontinuous large missing areas, in an adversarial manner.
By a maximum a posteriori (MAP) estimation, we formulate a new regularization term according to the log-likelihood function of the mixture model.
We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework.
In the proposed workflow, the user starts with an input image and applies a few intuitive transforms (e. g., colorization, image inpainting) within a 2D image editor of their choice, and in the next step, our technique produces a photorealistic result that approximates this target image.
Image extension models have broad applications in image editing, computational photography and computer graphics.