Fractals everywhere
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal image compression: theory and application
Fractal image compression: theory and application
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal Imaging
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Simple, General Model for the Affine Self-similarity of Images
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
IEEE Transactions on Image Processing
Solving the inverse problem of image zooming using "self-examples"
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Material detection based on fractal approach
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
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We consider the role of scalein the context of the recently-developed non-local-means (NL-means) filter. A new example-based variant of the NL-means is introduced and results based on same-scale and cross-scale counterparts will be compared for a set of images. We consider the cases in which neighborhoods are taken from the observed image itself as well as from other irrelevant images, varying the smoothness parameter as well. Our experiments indicate that using cross-scale (i.e., downsampled) neighborhoods in the NL-means filter yields results that are quite comparable to those obtained by using neighborhoods at the same-scale.