Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Image Denoising in High Dimensional Space


Non-Local Mean (NLM) Filter is commonly used for filtering Cryo-Electron Tomographic data which is contaminated by mixture of Gaussian and Poisson noise distributions at the projections level, while the noise distribution in the whole tomogram (after 3D reconstruction) is a little bit complex. The NLM is computationally expensive, especially for 3D data. In this project, we are going to investigate the efficiency of dimensionality reduction on sparse representation of the data to find the distance between blocks in lower dimensional space, which can be used for image denoising.

The following diagram shows you the overall idea of denoising the Cryo-ET data:

Project Phases

Phase Period Conducted by Materials
Intro. to CryoET
A Review of Image Denoising Algorithms
3-10 Nov El-Fiky Slides
Journal Paper 2005
Denoising via Learned Dictionaries 10-17 Nov El-Fiky Paper:CVPR 2006
Principal components for non-local means image denoising TBA El-Fiky Paper: ICIP 2008
Dimensionality Reduction and Manifold Learning TBA El-Fiky Slides Paper
Bonus: Sparse Subspace Denoising for Image Manifolds TBA El-Fiky Paper: CVPR 2013

Programming Language


What you are going to learn:

  • Clustering and Dictionary Learning
  • Figure out the Linear/Non-linear Dimensionality Reduction techniques

Useful Links

Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 09 Nov 2014 - 19:06 - ShadiAlbarqouni