Multifractal formalism for functions part I: results valid for all functions
SIAM Journal on Mathematical Analysis
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Probability Models for Clutter in Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texturing and Modeling: A Procedural Approach
Texturing and Modeling: A Procedural Approach
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
Image resolution enhancement via data-driven parametric models in the wavelet space
Journal on Image and Video Processing
Infinitely Divisible Cascades to Model the Statistics of Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet leaders and bootstrap for multifractal analysis of images
Signal Processing
Image Resolution Enhancement with Hierarchical Hidden Fields
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
On the role of exponential splines in image interpolation
IEEE Transactions on Image Processing
Virtual resolution enhancement of scale invariant textured images using stochastic processes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
On non-scale-invariant infinitely divisible cascades
IEEE Transactions on Information Theory
Regularity-preserving image interpolation
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
Enlargement or reduction of digital images with minimum loss of information
IEEE Transactions on Image Processing
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We present a new method of magnification for textured images featuring scale invariance properties. This work is originally motivated by an application to astronomical images. One goal is to propose a method to quantitatively predict statistical and visual properties of images taken by a forthcoming higher resolution telescope from older images at lower resolution. This is done by performing a virtual super resolution using a family of scale invariant stochastic processes, namely compound Poisson cascades, and fractional integration. The procedure preserves the visual aspect as well as the statistical properties of the initial image. An augmentation of information is performed by locally adding random small scale details below the initial pixel size. This extrapolation procedure yields a potentially infinite number of magnified versions of an image. It allows for large magnification factors (virtually infinite) and is physically conservative: zooming out to the initial resolution yields the initial image back. The (virtually) super resolved images can be used to predict the quality of future observations as well as to develop and test compression or denoising techniques.