Learn More
Nonlocal-means (NL-means) is an image denoising method that replaces each pixel by a weighted average of all the pixels in the image. Unfortunately, the method requires the computation of the weighting terms for all possible pairs of pixels, making it computationally expensive. Some short-cuts assign a weight of zero to any pixel pairs whose neighbourhood(More)
In this paper we present a novel single-frame image zooming technique based on so-called " self-examples ". Our method combines the ideas of fractal-based image zooming, example-based zooming, and nonlocal-means image denoising in a consistent and improved framework. In Bayesian terms, this example-based zooming technique targets the MMSE estimate by(More)
This paper presents a general PDE-framework for registration of contrast enhanced images. The approach directly applies the idea of separating the contrast enhancement term from the images in the regularization terms. In our formulation, we stay consistent with existing non-parametric image registration techniques, however, we carry an additional contrast(More)
The goal of this paper is to present a novel recipe for de-formable image registration under varying illumination, as a natural extension of the demons algorithm. This generalization is derived directly from the optical-flow constraints in a variational formulation. Furthermore , our approach provides a new mathematical interpretation of the demons(More)
The goal of this paper is to present a new recipe for the fractal image decoding process. In this paper, we explain how fractal-based methods can be internally combined with regularization schemes, e.g., Tikhonov, total variation (TV), or hard-constrained regularization. Although the regularization procedure is very common in context of algebraic image(More)