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A Stochastic Minimum-Norm Approach to Image and Texture Interpolation

Related publications (43)

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Multiscale Active Contours

Jean-Philippe Thiran, Pierre Vandergheynst, Xavier Bresson

In this paper, we propose an evolution equation for the active contours in scale spaces. This evolution equation is based on the Polyakov functional that has been first introduced in physics and has been then used in image processing in ...

SPIE2005

Adapting the Sample Size in Texture Synthesis

Sabine Süsstrunk, Luciano Sbaiz, Roberto Costantini

Starting from a sample of a given size, texture synthesis algorithms are used to create larger texture images. A good algorithm produces synthesized textures that are pixelwise different but perceptually indistinguishable from the original image. The sampl ...

2005

Rate-distortion optimized geometrical image processing

Rahul Shukla

Since geometrical features, like edges, represent one of the most important perceptual information in an image, efficient exploitation of such geometrical information is a key ingredient of many image processing tasks, including compression, denoising and ...

EPFL2004

Linear Interpolation Revitalized

Michaël Unser, Philippe Thévenaz, Thierry Blu

We present a simple, original method to improve piecewise-linear interpolation with uniform knots: we shift the sampling knots by a fixed amount, while enforcing the interpolation property. We determine the theoretical optimal shift that maximizes the qual ...

IEEE2004

Multiscale Active Contours

Jean-Philippe Thiran, Pierre Vandergheynst, Xavier Bresson

In this paper, we propose two approaches to define an evolution equation for the active contours in scale spaces. The evolution equation is based on the Polyakov functional that has been first introduced in physics and has been then used in image processin ...

2004

Multiwavelet-Like Bases for High Quality Image Interpolation

Michaël Unser, Thierry Blu

We present a simple but generalized interpolation method for digital images that uses multiwavelet-like basis functions. Most of interpolation methods uses only one symmetric basis function; for example, standard and shifted piecewise-linear interpolations ...

SPIE2003

Splines and Wavelets: New Perspectives for Pattern Recognition

Michaël Unser

We provide an overview of spline and wavelet techniques with an emphasis on applications in pattern recognition. The presentation is divided in three parts. In the first one, we argue that the spline representation is ideally suited for all processing task ...

DAGM e. V2003

Splines: A Perfect Fit for Medical Imaging

Michaël Unser

Splines, which were invented by Schoenberg more than fifty years ago, constitute an elegant framework for dealing with interpolation and discretization problems. They are widely used in computer-aided design and computer graphics, but have been neglected i ...

SPIE2002

Filter Design for Filtered Back-Projection Guided by the Interpolation Model

Michaël Unser

We consider using spline interpolation to improve the standard filtered back-projection (FBP) tomographic reconstruction algorithm. In particular, we propose to link the design of the filtering operator with the interpolation model that is applied to the s ...

SPIE2002

How a Simple Shift Can Significantly Improve the Performance of Linear Interpolation

Michaël Unser, Philippe Thévenaz, Thierry Blu

We present a simple, original method to improve piecewise linear interpolation with uniform knots: We shift the sampling knots by a fixed amount, while enforcing the interpolation property. Thanks to a theoretical analysis, we determine the optimal shift t ...

IEEE2002