@Article{ KitchingAGHHMRSVBBBBCGHHHKKKMMNPRRSSSTVvWW2011,
title = {Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook},
journal = {Annals of Applied Statistics},
year = {2011},
month = {9},
volume = {5},
number = {3},
pages = {2231-2263},
abstract = {GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a variable convolution kernel, pixelization and noise. This is the second in a series of challenges set to the astronomy, computer science and statistics communities, providing a structured environment in which methods can be improved and tested in preparation for planned astronomical surveys. GREAT10 extends upon previous work by introducing variable fields into the challenge. The “Galaxy Challenge” involves the precise measurement of galaxy shape distortions, quantified locally by two parameters called shear, in the presence of a known convolution kernel. Crucially, the convolution kernel and the simulated gravitational lensing shape distortion both now vary as a function of position within the images, as is the case for real data. In addition, we introduce the “Star Challenge” that concerns the reconstruction of a variable convolution kernel, similar to that in a typical astronomical observation. This document details the GREAT10 Challenge for potential participants. Continually updated information is also available from www.greatchallenges.info.},
file_url = {fileadmin/user_upload/files/publications/2011/GREAT10.pdf},
web_url = {http://projecteuclid.org/euclid.aoas/1318514302},
state = {published},
DOI = {10.1214/11-AOAS484},
author = {Kitching T, Amara A, Gill M, Harmeling S{harmeling}{Department Empirical Inference}, Heymans C, Massey R, Rowe B, Schrabback T, Voigt L, Balan S, Bernstein G, Bethge M{mbethge}{Research Group Computational Vision and Neuroscience}, Bridle S, Courbin F, Gentile M, Heavens A, Hirsch M{mhirsch}{Department Empirical Inference}, Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience}, Kiessling A, Kirk D, Kuijken K, Mandelbaum R, Moghaddam B, Nurbaeva G, Paulin-Henriksson S, Rassat A, Rhodes J, Sch\"olkopf B{bs}{Department Empirical Inference}, Shawe-Taylor J, Shmakova M, Taylor A, Velander M, van Waerbeke L, Witherick D and Wittman D}
}

@Article{ 6687,
title = {Lower bounds on the redundancy of natural images},
journal = {Vision Research},
year = {2010},
month = {10},
volume = {50},
number = {22},
pages = {2213-2222},
abstract = {The light intensities of natural images exhibit a high degree of redundancy. Knowing the exact amount of their statistical dependencies is important for biological vision as well as compression and coding applications but estimating the total amount of redundancy, the multi-information, is intrinsically hard. The common approach is to estimate the multi-information for patches of increasing sizes and divide by the number of pixels. Here, we show that the limiting value of this sequence---the multi-information rate---can be better estimated by using another limiting process based on measuring the mutual information between a pixel and a causal neighborhood of increasing size around it. Although in principle this method has been known for decades, its superiority for estimating the multi-information rate of natural images has not been fully exploited yet. Either method provides a lower bound on the multi-information rate, but the mutual information based sequence converges much faster to the multi-information r
ate than the conventional method does. Using this fact, we provide improved estimates of the multi-information rate of natural images and a better understanding of its underlying spatial structure.},
file_url = {/fileadmin/user_upload/files/publications/HosseiniEtAl2009_[0].pdf},
web_url = {http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6T0W-50RP1WF-1-4D&_cdi=4873&_user=29041&_pii=S004269891000372X&_origin=search&_coverDate=10%2F28%2F2010&_sk=999499977&view=c&w},
state = {published},
DOI = {10.1016/j.visres.2010.07.025},
author = {Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience}, Sinz FH{fabee}{Research Group Computational Vision and Neuroscience}{Research Group Computational Vision and Neuroscience} and Bethge M{mbethge}{Research Group Computational Vision and Neuroscience}}
}

@Article{ 6340,
title = {Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing},
journal = {Monthly Notices of the Royal Astronomical Society},
year = {2010},
month = {7},
volume = {405},
number = {3},
pages = {2044-2061},
abstract = {We present the results of the GREAT08 Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. The primary goal was to
stimulate new ideas by presenting the problem to researchers outside the shear measurement community. Six GREAT08 Team methods were presented at the launch of
the Challenge and five additional groups submitted results during the 6 month competition. Participants analyzed 30 million simulated galaxies with a range in signal to
noise ratio, point-spread function ellipticity, galaxy size, and galaxy type. The large quantity of simulations allowed shear measurement methods to be assessed at a level
of accuracy suitable for currently planned future cosmic shear observations for the first time. Different methods perform well in different parts of simulation parameter space and come close to the target level of accuracy in several of these. A number of fresh ideas have emerged as a result of the Challenge including a re-examination of the process of combining information from different galaxies, which reduces the dependence on realistic galaxy modelling. The image simulations will become increasingly sophis-
ticated in future GREAT challenges, meanwhile the GREAT08 simulations remain as a benchmark for additional developments in shear measurement algorithms.},
web_url = {http://www3.interscience.wiley.com/cgi-bin/fulltext/123456253/PDFSTART},
state = {published},
DOI = {10.1111/j.1365-2966.2010.16598.x},
author = {Bridle S, Balan ST, Bethge M{mbethge}{Research Group Computational Vision and Neuroscience}, Gentile M, Harmeling S{harmeling}{Department Empirical Inference}, Heymans C, Hirsch M{mhirsch}{Department Empirical Inference}, Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience}, Jarvis M, Kirk D, Kitching T, Kuijken K, Lewis A, Paulin-Henriksson S, Sch\"olkopf B{bs}{Department Empirical Inference}, Velander M, Voigt L, Witherick D, Amara A, Bernstein G, Courbin F, Gill M, Heavens A, Mandelbaum R, Massey R, Moghaddam B, Rassat A, Refregier A, Rhodes J, Schrabback T, Shawe-Taylor J, Shmakova M, van Waerbeke L and Wittman D}
}

@Article{ 5045,
title = {Almost Perfect Reconstruction Filter Bank for Non-redundant, Approximately Shift-Invariant, Complex Wavelet Transforms},
journal = {Journal of Wavelet Theory and Applications},
year = {2008},
month = {11},
volume = {2},
number = {1},
pages = {1-14},
abstract = {This paper presents a new filter bank design technique which leads to the shift invariant
representation of signals. The proposed wavelet transform due to the specific power spectrum of its filters has oriented filters in higher dimensions. Unlike
previous approaches, its filters do not have serious distributed bumps in the wrong side of the power spectrum and, simultaneously, they do not introduce any redundancy to the original signal. The proposed filter bank has linear phase filters
and high vanishing moment property. The simulation results show promising properties of the proposed filter bank that can be exploited in different signal processing applications.},
file_url = {/fileadmin/user_upload/files/publications/hosseini_%20complex%20wavelet_06_5045[1].pdf},
web_url = {http://www.ripublication.com/Volume/jwtav2n1.htm},
state = {published},
author = {Hosseini R{hosseini} and Vafadust M}
}

@Techreport{ 6114,
title = {Spectral Stacking: Unbiased Shear Estimation for Weak Gravitational Lensing},
year = {2009},
month = {10},
number = {186},
abstract = {We present a new method for the estimation of shear in gravitational lensing from a set of galaxy images with unknown distribution of shapes. Common procedures first compute an estimate of some characteristic feature for each individual galaxy and then average over these. The average can be used to estimate the shear as it becomes independent  of the individual galaxy shapes with increasing number of images. A common problem of the previous methods is that the estimators of the features are biased. Here we introduce ``{it spectral stacking}‘‘ which uses the power spectrum as a characteristic feature of the individual galaxies. If the galaxy images are contaminated by Poisson noise, an unbiased estimator of the power spectrum exists which is used in the  analysis. Furthermore, the power spectrum is independent of the location of the individual galaxy centers provided the smoothed galaxy intensities decay sufficiently fast.  No further assumptions are necessary. The alg
orithm won the main contest of the Great08 challenge.},
file_url = {/fileadmin/user_upload/files/publications/MPIK-TR-186_6114[0].pdf},
state = {published},
author = {Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience} and Bethge M{mbethge}{Research Group Computational Vision and Neuroscience}}
}

@Poster{ TheisHB2012,
title = {Mixtures of conditional Gaussian scale mixtures: the best
model for natural images},
journal = {Frontiers in Computational Neuroscience},
year = {2012},
month = {9},
volume = {Conference Abstract: Bernstein Conference 2012},
pages = {247},
abstract = {Modeling the statistics of natural images is a common problem in computer vision and computational neuroscience. In computational neuroscience, natural image models are used as a means to understand the input to the visual system as well as the visual system’s internal representations of the visual input.
Here we present a new probabilistic model for images of arbitrary size. Our model is a directed graphical model based on mixtures of Gaussian scale mixtures. Gaussian scale mixtures have been repeatedly shown to be suitable building blocks for capturing the statistics of natural images, but have not been applied in a directed modeling context. Perhaps surprisingly—given the much larger popularity of the undirected Markov random field approach—our directed model yields unprecedented performance when applied to natural images while also being easier to train, sample and evaluate.
Samples from the model look much more natural than samples of other models and capture many long-range higher-order correlations. When trained on dead leave images or textures, the model is able to reproduce many properties of these as well—showing the flexibility of our model. By extending the model to multiscale representations, it is able to reproduce even longer-range correlations.
An important measure to quantify the amount of correlations captured by a model is the average log-likelihood. We evaluate our model as well as several other patch-based and whole-image models and show that it yields the best performance reported to date when measured in bits per pixel. A problem closely related to image modeling is image compression. We show that our model can compete even with some of the best image compression algorithms.},
web_url = {http://www.frontiersin.org/10.3389/conf.fncom.2012.55.00079/event_abstract},
event_name = {Bernstein Conference 2012},
event_place = {München, Germany},
state = {published},
DOI = {10.3389/conf.fncom.2012.55.00079},
author = {Theis LM{lucas}{Research Group Computational Vision and Neuroscience}, Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience} and Bethge M{mbethge}{Research Group Computational Vision and Neuroscience}}
}

@Poster{ TheisHB2011,
title = {A multiscale model of natural images},
year = {2011},
month = {10},
volume = {12},
pages = {43},
abstract = {We present a probabilistic model for natural images which is based on Gaussian scale mixtures
and a simple multiscale representation. In contrast to the dominant approach to modeling
whole images focusing on Markov random fields, we formulate our model in terms of a directed
graphical model. We show that it is able to generate images with interesting higher-order
correlations when trained on natural images or samples from an occlusion based model. More
importantly, the directed model enables us to perform a principled evaluation. While it is
easy to generate visually appealing images, we demonstrate that our model also yields the
best performance reported to date when evaluated with respect to the cross-entropy rate, a
measure tightly linked to the average log-likelihood.},
web_url = {http://www.neuroschool-tuebingen-nena.de/index.php?id=284},
event_name = {12th Conference of Junior Neuroscientists of Tübingen (NeNA 2011)},
event_place = {Heiligkreuztal, Germany},
state = {published},
author = {Theis L{lucas}{Research Group Computational Vision and Neuroscience}, Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience} and Bethge M{mbethge}{Research Group Computational Vision and Neuroscience}}
}

@Poster{ 6703,
title = {New Estimate for the Redundancy of Natural Images},
journal = {Frontiers in Computational Neuroscience},
year = {2010},
month = {10},
volume = {Conference Abstract: Bernstein Conference on Computational Neuroscience},
abstract = {The light intensities of natural images exhibit a high degree of redundancy. Knowing the exact amount of their statistical dependencies is important for biological vision as well as compression and coding applications but estimating the total amount of redundancy, the multi-information, is intrinsically hard. The conventional approach for estimating the redundancy per pixel is to estimate the multi-information for patches of increasing sizes and divide by the number of pixels. Here, we show that the limiting value of this sequence---the multi-information rate---can be better estimated by another limiting process based on measuring the mutual information between a pixel and a causal neighborhood of increasing size around it. We explain the theoretical relationship of the two methods and compare their performance on natural images. While both methods provide a lower bound on the multi-information rate, the mutual information based sequence converges much faster to the multi-information rate than the conventional method does. In this way we can provide improved estimates of the multi-information rate of natural images and a better understanding its underlying spatial structure. In addition, we will present work in progress on hierarchical model architectures that has led to further improvements of this lower bound.},
web_url = {http://www.frontiersin.org/10.3389/conf.fncom.2010.51.00006/event_abstract},
event_name = {Bernstein Conference on Computational Neuroscience (BCCN 2010)},
event_place = {Berlin, Germany},
state = {published},
DOI = {10.3389/conf.fncom.2010.51.00006},
author = {Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience}, Sinz F{fabee}{Research Group Computational Vision and Neuroscience}{Research Group Computational Vision and Neuroscience} and Bethge M{mbethge}{Research Group Computational Vision and Neuroscience}}
}

@Patent{ 5492,
title = {Method and Device for Image Compression},
year = {2009},
month = {12},
number = {WO/2009/146933},
abstract = {A method for compressing a digital image comprises the steps of:selecting an image patch of the digital image; assigning the selected image patch to a specific class (z); transforming the image patch, with a pre-determined class-specific transformation function; and quantizing the transformed image patch.},
web_url = {http://www.wipo.int/pctdb/en/wo.jsp?WO=2009146933},
state = {published},
author = {Bethge M{mbethge}{Research Group Computational Vision and Neuroscience} and Hosseini R{hosseini}{Research Group Computational Vision and Neuroscience}}
}