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Nonlocal-means (NL-means) is currently one of the top image denoising methods, replacing 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 shortcuts assign a weight of zero to any pixel pairs… (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)

We introduce a novel super-resolution scheme for multi-frame image sequences. Our method is closely associated with the recently developed " non-local-means denoising filter ". In the proposed algorithm, no explicit motion estimation is performed, unlike in many other methods. Our results are comparable, if not superior, to many existing approaches,… (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)

We show how fractal image coding can be viewed and generalized in terms of the method of projections onto convex sets (POCS). In this approach, the fractal code defines a set of spatial domain similarity constraints. We also show how such a reformulation in terms of POCS allows additional contraints to be imposed during fractal image decoding. Two… (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)

We construct a complete metric space (Y, dY) of measure-valued images, µ : X → M(Rg), where X is the base or pixel space and M(Rg) is the set of probability measures supported on the greyscale range Rg. Such a formalism is well-suited to nonlocal image processing, i.e., the manipulation of the value of an image function u(x) based upon values u(y k)… (More)

Empirical results from a qualitative study within an important North American aircraft engine manufacturing company allows us to propose an alternative framework for achieving effective knowledge sharing based on the boundary construction concept. Tapping into Latour's [13] epistemological insights of mutual transformation, hybridization and… (More)