Mehran Ebrahimi

Efficient nonlocal-means denoising using the SVD

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 averages are too dissimilar. In this paper, we propose an alternative strategy that uses the SVD to more efficiently eliminate pixel pairs that are dissimilar. Experiments comparing this method against other NL-means speed-up strategies show that its refined discrimination between similar and dissimilar pixel neighbourhoods significantly improves the denoising effect.

Measure-Valued Images, Associated Fractal Transforms, and the Affine Self-Similarity of Images

We construct a complete metric space

Examining the Role of Scale in the Context of the Non-Local-Means Filter

(Y,d_Y)

Image registration for abdominal dynamic contrast-enhanced magnetic resonance images

of measure-valued images,

A General PDE-Framework for Registration of Contrast Enhanced Images

\mu : X \to {\cal M}(\mathbb{R}_g)

Fractal image coding as projections onto convex sets

, where

Deformable Image Registration and Intensity Correction of Cardiac Perfusion MRI

X

Image Registration under Varying Illumination: Hyper-Demons Algorithm

is the base or pixel space and

Regularized Fractal Image Decoding

{\cal M}(\mathbb{R}_g)

A Gauss-Newton approach to joint image registration and intensity correction

is the set of probability measures supported on the greyscale range