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Christopher Burger

Address: Spemannstr. 38
72076 Tübingen
Room number: 203
Phone: +49 7071 601 535
Fax: +49 7071 601 552
E-Mail: christopher.burger
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Christopher Burger

Position: PhD Student  Unit: Alumni Schölkopf

I'm working on image denoising, which is the problem of finding a clean image, given a noisy one. Noise in images arises for a number of reasons, including imperfect digital image sensors. The problem is of growing importance due to an explosion in the number of digital images recorded every day and the fact that all digital images contain some amount of noise. My personal web-page is kept more up-to-date.

 

My research can be divided into three main categories:

  • Astronomical image denoising with a pixel-specific noise model.
    For digital photographsof astronomical objects, where exposure times are long, the dark-current noise is a signi cantsource of noise. Usually, denoising methods assume additive white Gaussian noise, with equal variance for each pixel. However, dark-current noise has diff erent properties for every pixel. We use a pixel-speci c noise model to handle dark-current noise, as well as an image prior adapted to astronomical images. Our method is shown to perform well in a laboratory environment, and produces visually appealing results in a real-world setting.

 

  • A multi-scale meta-procedure for improving existing denoising algorithms.
    Most denoising algorithms focus on recovering high frequencies. However, for high noise levels it is also important to recover low frequencies. We present a multi-scale meta-procedure that applies existing denoising algorithms across di fferent scales and combines the resulting images into a single denoised image. We show that our method can improve the results achieved by many denoising algorithms.

 

  • State-of-the-art image denoising with multi-layer perceptrons.
    Many of the best-performing denoising methods rely on a cleverly engineered algorithm. In contrast, we take a learning approach to denoising and train a multi-layer perceptron to denoise image patches. Using this approach, we outperform the previous state-of-the-art. Our approach also achieves results that are superior to one type of theoretical bound and goes a large way toward closing the gap with a second type of theoretical bound. Furthermore, we achieve outstanding results on other types of noise, including JPEG-artifacts and Poisson noise. Also, we show that multilayer perceptrons can be used to combine the results of several denoising algorithms. This approach often yields better results than the best method in the combination. We discuss in detail which trade-off s have to be considered during the training procedure. We are also able to make observations regarding the functioning principle of multi-layer perceptrons for image denoising.

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Articles (3):

Cavusoglu M Person, Pohmann R Person, Burger HC Person and Uludag K Person (2012) Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling Magnetic Resonance in Medicine Epub ahead.
Burger HC Person, Schuler CJ Person and Harmeling S Person (2012) Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds . submitted
Burger HC Person, Schuler CJ Person and Harmeling S Person (2012) Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms . submitted

Conference papers (5):

Schuler C Person, Burger HC Person, Harmeling S Person and Schölkopf B Person (2013) A machine learning approach for image deconvolution IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). accepted
Burger HC Person, Schuler CJ Person and Harmeling S Person (2012) Image denoising: Can plain Neural Networks compete with BM3D? 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), 2392 - 2399.
pdf
Burger HC Person and Harmeling S Person (2011) Improving Denoising Algorithms via a Multi-scale Meta-procedure In: Pattern Recognition, (Ed) Mester, R. , M. Felsberg, 33rd DAGM Symposium, Springer, Berlin, Germany, 206-215.
Burger HC Person, Schölkopf B Person and Harmeling S Person (2011) Removing noise from astronomical images using a pixel-specific noise model (Ed) H Lensch, SL Narasimhan and ME Testorf, IEEE International Conference on Computational Photography (ICCP 2011), IEEE, Piscataway, NJ, USA, 8 pages.
Malon H , Miller M , Burger HC Person, Cosatto E and Graf HP (2008) Identifying histological elements with convolutional neural networks (Ed) Chbeir,R. , Y. Badr, A. Abraham, D. Laurent, M. Köppen, F. Ferri, L.A. Zadeh, Y. Ohsawa, 5th International Conference on Soft Computing as Transdisciplinary Science and Technology (CSTST '08), ACM Press, New York, NY, USA, 450-456.

Theses (1):

Burger HC Person: Modelling and Learning Approaches to Image Denoising, Eberhard Karls Universität Tübingen, Germany, (2013). PhD thesis

Patent (1):

Cosatto E , Burger HC Person and Miller ML : Mitotic Figure Detector and Counter System and Method for Detecting and Counting Mitotic Figures, US 2010/0002920 A1, (2010).

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