News
Research overview
Computational photography
Machine learning
Applications
Miscellany
Publications
Multiframe blind deconvolution and superresolution

Online Multiframe Blind Deconvolution with Superresolution and Saturation Correction
M. Hirsch, S. Harmeling, S. Sra, B. Schölkopf
Astronomy and Astrophysics, 531 (A9), 2011
website and data sets


Multiframe Blind Deconvolution, SuperResolution, and Saturation Correction via Incremental EM
S. Harmeling, M. Hirsch, S. Sra, B. Schölkopf
IEEE International Conference on Image Processing (ICIP), Hong Kong, 2010


Efficient Filter Flow for spacevariant multiframe blind deconvolution
M. Hirsch, S. Sra, B. Schölkopf, S. Harmeling
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, USA, 2010


Online Blind Image Deconvolution for Astronomy
S. Harmeling, M. Hirsch, S. Sra, B. Schölkopf
IEEE Conference on Computational Photography (ICCP), San Francisco, USA, 2009
code (mbd0.0.tar)

Camera shake removal

Recording and playback of camera shake: benchmarking blind deconvolution with a realworld database
R. Köhler, M. Hirsch, B. Schölkopf, B. Mohler, S. Harmeling
European Conference on Computer Vision (ECCV), Firenze, Italy, 2012
website and benchmark data sets


Fast Removal of Nonuniform Camera Shake
M. Hirsch, C. Schuler, S. Harmeling, B. Schölkopf
International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011
website and images


SpaceVariant SingleImage Blind Deconvolution for Removing Camera Shake
S. Harmeling, M. Hirsch, B. Schölkopf
Conference on Neural Information Processing Systems (NIPS), Volume 22, Vancouver, Canada, 2010

Optical aberration removal

Blind Correction of Optical Aberrations
C. Schuler, M. Hirsch, S. Harmeling, B. Schölkopf
European Conference on Computer Vision (ECCV), Firenze, Italy, 2012
website and images


Nonstationary Correction of Optical Aberrations
C. Schuler, M. Hirsch, S. Harmeling, B. Schölkopf
International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011
website and images

Image deconvolution: miscellany

On a link between kernel mean maps and Fraunhofer diffraction, with an application to superresolution beyond the diffraction limit
S. Harmeling, M. Hirsch, B. Schölkopf
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, 2013


A machine learning approach for image deconvolution
C. Schuler, H.C. Burger, S. Harmeling, B. Schölkopf
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, USA, 2013
website and toolbox


Improving alpha matting and motion blurred foreground estimation
R. Köhler, M. Hirsch, B. Schölkopf, S. Harmeling
IEEE Conference on Image Processing (ICIP), Melbourne, Australia, 2013


Automatic Foreground Background Refocusing
A. Loktyushin, S. Harmeling
IEEE Conference on Image Processing (ICIP), Brussels, Belgium, 2011

Image denoising

Learning how to combine internal and external denoising methods
C. Burger, C. Schuler, S. Harmeling
Accepted at German Conference on Pattern Recognition (GCPR/DAGM), 2013


Image denoising with multilayer perceptrons, part 1: comparison with existing algorithms and with bounds
C. Burger, C. Schuler, S. Harmeling
arXiv:1211.1544 [cs.CV], 2012


Image denoising with multilayer perceptrons, part 2: training tradeoffs and analysis of their mechanisms
C. Burger, C. Schuler, S. Harmeling
arXiv:1211.1544 [cs.CV], 2012


Image denoising: Can plain Neural Networks compete with BM3D?
H.C. Burger, C. Schuler, S. Harmeling
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, USA, 2012
website with code and data


Improving Denoising Algorithms via a MultiScale MetaProcedure
C. Burger, S. Harmeling
Annual Symposium of German Association for Pattern Recognition (DAGM), Frankfurt, Germany 2011 (DAGM prize)


Removing noise from astronomical images using a pixelspecific noise model
C. Burger, B. Schölkopf, S. Harmeling
IEEE Conference on Computational Photography (ICCP), CMU, Pittsburgh, USA, 2011

Computer vision (other than deconvolution and denosing)
Linear and nonlinear ICA

How to test the quality of reconstructed sources in independent component analysis (ICA) of EEG/MEG Data
M. GrosseWentrup, S. Harmeling, T. Zander, J. Hill, B. Schölkopf
Pattern Recognition in Neuro Imaging (PRNI) Workshop, 2013


Inlierbased ICA with an application to superimposed images
F. Meinecke, S. Harmeling, and K.R. Müller
International Journal of Imaging Systems and Technology (IJIST), Volume 15, Issue 1, pp. 4855, 2005


Independent Component Analysis and beyond
S. Harmeling
Doktorarbeit (PhD thesis), supervisor Prof. Dr. K.R. Müller, Universität Potsdam, 2004


Editorial for the special section on ICA
E. Oja, S. Harmeling, and L. Almeida
Signal Processing, Volume 84, pp. 215216, 2004


Injecting noise for analysing the stability of ICA components
S. Harmeling, F. Meinecke, and K.R. Müller
Signal Processing, Volume 84, pp. 255266, 2004


Robust ICA for superGaussian sources
F. Meinecke, S. Harmeling, and K.R. Müller
Fifth International Conference on Independent Component Analysis and Blind Signal Separation (ICA), Granada, Spain, pp. 217224, 2004
code (ibica0.1.tar)


Using kernel PCA for initialisation of variational bayesian nonlinear blind source separation method
A. Honkela, S. Harmeling, L. Lundqvist, and H. Valpola
Fifth International Conference on Independent Component Analysis and Blind Signal Separation (ICA), Granada, Spain, pp. 790797, 2004


Blind separation of postnonlinear mixtures using linearizing transformations and temporal decorrelation
A. Ziehe, M. Kawanabe, S. Harmeling, and K.R. Müller
Journal of Machine Learning Research (JMLR), Volume 4 , pp. 13191338, 2003


Kernelbased nonlinear blind source separation
S. Harmeling, A. Ziehe, M. Kawanabe, and K.R. Müller
Neural Computation, Volume 15, pp. 10891124, 2003
code (ktdsep0.2.tar)


Blind separation of postnonlinear mixtures using gaussianizing transformations and temporal decorrelation
A. Ziehe, M. Kawanabe, S. Harmeling, and K.R. Müller
Fourth International Symposium on Independent Component Analysis and Blind Signal Separation (ICA), pp. 269274, Nara, Japan, 2003


Analysing ICA components by injecting noise
S. Harmeling, F. Meinecke, and K.R. Müller
Fourth International Symposium on Independent Component Analysis and Blind Signal Separation (ICA), pp. 149154, Nara, Japan, 2003
code (relica0.1.tar)


Kernel feature spaces and nonlinear blind source separation
S. Harmeling, A. Ziehe, M. Kawanabe, and K.R. Müller
Conference on Neural Information Processing Systems (NIPS), Volume 14, Vancouver, Canada, 2002


Nonlinear blind source separation using kernel feature spaces
S. Harmeling, A. Ziehe, M. Kawanabe, B. Blankertz, and K.R. Müller
Third Symposium on Independent Component Analysis and Blind Signal Separation (ICA), pp. 102107, San Diego, USA, 2001


Separation of postnonlinear mixtures using ACE and temporal decorrelation
A. Ziehe, M. Kawanabe, S. Harmeling, and K.R. Müller
Third Symposium on Independent Component Analysis and Blind Signal Separation (ICA), pp. 433438, San Diego, CA, 2001

Unsupervised learning (other than ICA)

Greedy Learning of Binary Latent Trees
S. Harmeling, C. K. I. Williams
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Volume 33, Issue 6, 2011
code (ltt1.4.tar)


Exploring model selection techniques for nonlinear dimensionality reduction
S. Harmeling
University of Edinburgh, School of Informatics Research Report EDIINFRR0960, 2007
code (nldim0.1.tar)


From outliers to prototypes: Ordering data
S. Harmeling, G. Dornhege, D. Tax, F. Meinecke, and K.R. Müller
Neurocomputing, Volume 69, Issues 1315, pp. 16081618, 2006

Supervised learning (see also computer vision)
Reinforcement learning

ExpectationMaximization methods for solving (PO)MDPs and optimal control problems
M. Toussaint, A. Storkey, S. Harmeling
Book chapter in ``Bayesian Time Series Models'', Edited by D. Barber, A.T. Cemgil, S. Chiappa, Cambridge University Press, 2011


Probabilistic inference for solving MDPs and POMDPs
M. Toussaint, S. Harmeling, A. Storkey
University of Edinburgh, School of Informatics Research Report EDIINFRR0934, 2006

Natural language processing
Image analysis in cosmology

Image analysis for cosmology: results from the GREAT10 Galaxy Challenge
T.D. Kitching, S.T. Balan, S. Bridle, N. Cantale, F. Courbin, T. Eifler,M. Gentile, M.S.S. Gill, S. Harmeling, C. Heymans, and 18 other authors
Monthly Notices of the Royal Astronomical Society, 423 (4), pp. 31633208, 2012


Gravitational Lensing Accuracy Testing 2010 Challenge Handbook
T. Kitching, A. Amara, M. Gill, S. Harmeling, C. Heymans, R. Massey, B. Rowe, T. Schrabback, L. Voigt, S. Balan, G. Bernstein, M. Bethge, S. Bridle, and 22 other authors
Annals of Applied Statistics, 5 (3), 2011


Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing
S. Bridle, S. T. Balan, M. Bethge, M. Gentile, S. Harmeling, C. Heymans, M. Hirsch, R. Hosseini, M. Jarvis, D. Kirk, T. Kitching, K. Kuijken, A. Lewis, S. PaulinHenriksson, B. Schölkopf, M. Velander, L. Voigt, D. Witherick, and 16 other authors
Monthly Notices of the Royal Astronomical Society, 405 (3), pp. 20442061, 2010

Earth system science / Ecoinformatics
Matrix differential calculus
Statistics, genetic algorithms
Mathematical logic / proof theory
Miscellaneous code
