P. Maass

发表

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

R. Casadonte, M. Kriegsmann, J. Kriegsmann, 2017, Biochimica et Biophysica Acta - Proteins and Proteomics.

Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction

Peter Maass, Maximilian Schmidt, Jens Behrmann, 2020, 2006.06270.

Deep learning for tumor classification in imaging mass spectrometry

Christian Etmann, Peter Maass, Jens Behrmann, 2017, Bioinform..

Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data pdf

Christian Etmann, Peter Maass, Maximilian Schmidt, 2019, ArXiv.

Function spaces and optimal currents in impedance tomography

P. Maass, Bangti Jin, T. Khan, 2011 .

The asymptotic behaviour of weak solutions to the forward problem of electrical impedance tomography on unbounded three‐dimensional domains

P. Maass, M. Pidcock, C. Sebu, 2009 .

Sparse 3D reconstructions in electrical impedance tomography using real data

P. Maass, C. Sebu, M. Gehre, 2014 .

Minimization of Tikhonov Functionals in Banach Spaces

P. Maass, F. Schöpfer, T. Schuster, 2008 .

Sparsity regularization for parameter identification problems

P. Maass, Bangti Jin, 2012 .

Norm sensitivity of sparsity regularization with respect to p

P. Maass, R. Strehlow, K. S. Kazimierski, 2012 .

A new approach to interpolating scaling functions

K. Gröchenig, P. Maass, S. Dahlke, 1999 .

The Affine uncertainty principle in one and two dimensions

P. Maass, S. Dahlke, 1995 .

Mathematical methods for forecasting bank transaction data

U. Parlitz, P. Maass, T. Köhler, 2003 .

Mathematical concepts of multiscale smoothing

K. Bredies, D. Lorenz, P. Maass, 2005 .

The Canonical Coherent States Associated With Quotients of the Ane Weyl-Heisenberg Group

G. Teschke, D. Lorenz, P. Maass, 2006 .

Regularizing properties of the Mumford–Shah functional for imaging applications

M. Jiang, P. Maass, Thomas Page, 2014 .

Inverse Problems and Parameter Identification in Image Processing

M. S. Diallo, J. F. Acker, H. Peitgen, 2008 .

Sparsity regularization in inverse problems

O. Scherzer, P. Maass, Bangti Jin, 2017 .

AN OUTLINE OF ADAPTIVE WAVELET GALERKIN METHODS FOR TIKHONOV REGULARIZATION OF INVERSE PARABOLIC PROBLEMS

P. Maass, S. Dahlke, 2003 .

A Review of Some Modern Approaches to the Problem of Trend Extraction

P. Maass, E. Dagum, Silvia Bianconcini, 2012 .

Tikhonov regularization for electrical impedance tomography on unbounded domains

P. Maass, M. Pidcock, Michael Lukaschewitsch, 2003 .

An analysis of electrical impedance tomography with applications to Tikhonov regularization

P. Maass, Bangti Jin, 2012 .

MRI-compatible pipeline for three-dimensional MALDI imaging mass spectrometry using PAXgene fixation.

Stefan Heldmann, Michael Becker, Theodore Alexandrov, 2013, Journal of proteomics.

A mollifier method for linear operator equations of the first kind

Peter Maass, Alfred K. Louis, P. Maass, 1990 .

Optimization of Catalytically Active Sites Positioning in Porous Cathodes of Lithium/Air Batteries Filled with Different Electrolytes

P. Maass, T. Dabrowski, A. Struck, 2015 .

An adaptive wavelet method for parameter identification problems in parabolic partial differential equations

Stephan Dahlke, Thorsten Raasch, S. Dahlke, 2022 .

Singular value decompositions for Radon transforms

P. Maass, 1991 .

I V ] 1 5 O ct 2 01 8 Joint bimodal image reconstruction of DOT and XCT with an extended Mumford-Shah functional

M. Jiang, P. Maass, A. Louis, 2018 .

Hybrid Regularization and Sparse Reconstruction of Imaging Mass Spectrometry Data

D. Trede, P. Maass, T. Alexandrov, 2013 .

Wavelet-Galerkin methods for ill-posed problems

P. Maass, V. Dicken, V. DICKEN, 1996 .

Wavelet accelerated iteration methods for hyperthermia treatment planning

Ronny Ramlau, Peter Maaß, P. Maass, 1996 .

A generalized conditional gradient method for nonlinear operator equations with sparsity constraints

Dirk A. Lorenz, Kristian Bredies, Peter Maass, 2007 .

Zentrum für Technomathematik Fachbereich 3 – Mathematik und Informatik Sparsity regularization for electrical impedance tomography : well-posedness and convergence rates

P. Maass, P. Q. Muoi, 2011 .

Sparsity regularization of the diffusion coefficient problem: well-posedness and convergence rates

P. Maass, P. Q. Muoi, 2011 .

Zentrum für Technomathematik Fachbereich 3 – Mathematik und Informatik Semismooth Newton and Quasi-Newton Methods in Weighted l 1 − Regularization of Nonlinear Inverse Problems

P. Maass, P. Q. Muoi, 2011 .

Atmospheric inverse modeling via sparse reconstruction

Scot M. Miller, P. Maass, J. Notholt, 2016 .

Atmospheric inverse modeling via sparse reconstruction

Scot M. Miller, P. Maass, J. Notholt, 2016 .

An approach to optimize sample preparation for MALDI imaging MS of FFPE sections using fractional factorial design of experiments

Andrew D. Palmer, T. Boskamp, Delf Lachmund, 2016, Analytical and Bioanalytical Chemistry.

A regularized solution for the inverse conductivity problem using mollifiers

P. Maass, M. Pidcock, C. Sebu, 2010 .

Surface Generation Process with Consideration of the Balancing State in Diamond Machining

Ekkard Brinksmeier, Jost Vehmeyer, Ronny Ramlau, 2013 .

The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods

P. Maass, Maximilian Schmidt, Johannes Leuschner, 2019, ArXiv.

Interpolating Refinable Functions And Wavelets For General Scaling Matrices

P. Maass, S. Dahlke, 1997 .

2D and 3D MALDI-imaging: conceptual strategies for visualization and data mining.

Stefan Heldmann, Bernd Fischer, Herbert Thiele, 2014, Biochimica et biophysica acta.

Proteomic Analysis of the Spatio-temporal Based Molecular Kinetics of Acute Spinal Cord Injury Identifies a Time- and Segment-specific Window for Effective Tissue Repair*

J. H. Kobarg, I. Fournier, P. Maass, 2016, Molecular & Cellular Proteomics.

Reconstruction of Radar reflectivity densities in a narrowband regime

G. Teschke, P. Maass, S. Dahlke, 2004, IEEE Transactions on Antennas and Propagation.

Multiscale Approximation

G. Teschke, H. Thiele, P. Maass, 2007 .

Smoothed projection methods for the moment problem

P. Maass, A. Louis, 1991 .

High-resolution neutron small-angle scattering with a double-crystal diffractometer and 2D reconstruction

P. Maass, W. Treimer, U. Feye-Treimer, 1991 .

StainCUT: Stain Normalization with Contrastive Learning

José Carlos Gutiérrez Menéndez, P. Maass, D. O. Baguer, 2022, J. Imaging.

Robust subtyping of non‐small cell lung cancer whole sections through MALDI mass spectrometry imaging

Jens Behrmann, R. Casadonte, M. Kriegsmann, 2022, Proteomics. Clinical applications.

SELTO: Sample-Efficient Learned Topology Optimization

P. Maass, Sören Dittmer, David Erzmann, 2022, ArXiv.

PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization

G. Steidl, P. Maass, Alexander Denker, 2022, ArXiv.

Mathematical aspects of catalyst positioning in lithium/air batteries

P. Maass, L. Ciacchi, Dinh Nho Hào, 2020, Inverse Problems.

A Survey on Surrogate Approaches to Non-negative Matrix Factorization

P. Maass, Pascal Fernsel, 2018, Vietnam Journal of Mathematics.

Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems

P. Maass, U. Iben, A. Rademacher, 2022, ArXiv.

Regularization by Architecture: A Deep Prior Approach for Inverse Problems

P. Maass, Sören Dittmer, Daniel Otero Baguer, 2018, Journal of Mathematical Imaging and Vision.

An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction

P. Maass, Alexander Denker, Maximilian Schmidt, 2021, 2111.11926.

Numerical experiments with MALDI Imaging data

J. H. Kobarg, P. Vandergheynst, J. Oetjen, 2013, Advances in Computational Mathematics.

Love Thy Neighbor: A Story of War

P. Maass, 1996 .

Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI

R. Casadonte, M. Kriegsmann, J. Kriegsmann, 2022, Cancers.

A numerical method for finding the convex hull of cavities

P. Maass, M. Pidcock, S. Siltanen, 2017 .

PatchNR: learning from very few images by patch normalizing flow regularization

G. Steidl, P. Maass, Alexander Denker, 2022, Inverse Problems.

Signal representation, uncertainty principles and localization measures

B. Torrésani, P. Maass, H. Stark, 2014, Advances in Computational Mathematics.

Solving inverse problems using data-driven models

Simon R. Arridge, Ozan Öktem, Peter Maass, 2019, Acta Numerica.

Inverse Problems in Designing New Structural Materials

Iwona Piotrowska-Kurczewski, Peter Maass, Daniel Otero Baguer, 2020, Modeling, Simulation and Optimization of Complex Processes HPSC 2018.

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

Kees Joost Batenburg, Maureen van Eijnatten, Peter Maass, 2021, J. Imaging.

Corrosion and Corrosion Protection

P. Maass, 2011 .

Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time

Jean Le'Clerc Arrastia, P. Maass, Maximilian Schmidt, 2023, ArXiv.

Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry

Franziska Hoffmann, Orlando Guntinas-Lichius, Kirill Veselkov, 2015, GigaScience.

Benchmark datasets for 3 D MALDI-and DESI-imaging mass spectrometry

Franziska Hoffmann, Orlando Guntinas-Lichius, Michael Becker, 2015 .

Wavelets - theory and applications

Andreas Rieder, Peter Maass, Alfred K. Louis, 1997, Pure and applied mathematics.

Reconstruction of Wideband Reflectivity Densities by Wavelet Transforms

Stephan Dahlke, Gerd Teschke, Peter Maass, 2003, Adv. Comput. Math..

Do Uncertainty Minimizers Attain Minimal Uncertainty?

Nir Sochen, Chen Sagiv, Peter Maass, 2010 .

The Uncertainty Principle Associated with the Continuous Shearlet Transform

Gitta Kutyniok, Stephan Dahlke, Chen Sagiv, 2008, Int. J. Wavelets Multiresolution Inf. Process..

Wavelets and digital image processing

Andreas Rieder, Peter Maaß, Alfred K. Louis, 1994 .

An adaptive discretization for Tikhonov-Phillips regularization with a posteriori parameter selection

Ronny Ramlau, Sergei V. Pereverzyev, Peter Maass, 2001, Numerische Mathematik.

Wavelet-Accelerated Tikhonov-Phillips Regularization with Applications

Andreas Rieder, Peter Maaß, P. Maass, 1997 .

3D MALDI mass spectrometry imaging reveals specific localization of long-chain acylcarnitines within a 10-day time window of spinal cord injury

I. Fournier, P. Maass, L. Hauberg-Lotte, 2018, Scientific Reports.

Ground Truth Free Denoising by Optimal Transport pdf

Carola-Bibiane Schönlieb, Peter Maass, Sören Dittmer, 2020, Numerical Algebra, Control and Optimization.

Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset pdf

Tobias Kluth, Peter Maass, Soren Dittmer, 2020, ArXiv.

Joint Bi-Modal Image Reconstruction Of Dot And Xct With An Extended Mumford-Shah Functional

Di He, Ming Jiang, Peter Maass, 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

A Survey on Surrogate Approaches to Non-negative Matrix Factorization

Pascal Fernsel, Peter Maass, P. Maass, 2018, Vietnam Journal of Mathematics.

Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction

Tobias Kluth, Peter Maass, P. Maass, 2017 .

Improved image reconstruction in magnetic particle imaging using structural a priori information

Tobias Kluth, Peter Maaß, Christine Bathke, 2017 .

An iterative regularization method for nonlinear problems based on Bregman projections

P. Maass, R. Strehlow, 2016 .

Compressed sensing in imaging mass spectrometry

Peter Maaß, Theodore Alexandrov, Dennis Trede, 2013 .

Trend-specific clustering for micro mass production of linked parts

Kirsten Tracht, Peter Maaß, Ann-Kathrin Onken, 2018 .

Inverting Prediction Models in Micro Production for Process Design

Daniel Rippel, Michael Lütjen, Iwona Piotrowska-Kurczewski, 2018 .

Least-squares Based Parameter Identification for a Function-related Surface Optimisation in Micro Ball-end Milling☆

Oltmann Riemer, Jost Vehmeyer, Iwona Piotrowska-Kurczewski, 2015 .

An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction

P. Maass, Alexander Denker, Maximilian Schmidt, 2021, IEEE Transactions on Computational Imaging.

A mollifier method for the inverse conductivity problem

P. Maass, M. Pidcock, C. Sebu, 2008 .

Descent gradient methods for nonsmooth minimization problems in ill-posed problems

Dinh Nho Hào, Peter Maass, Michael Pidcock, 2016, J. Comput. Appl. Math..

GRADIENT DESCENT FOR TIKHONOV FUNCTIONALS WITH SPARSITY CONSTRAINTS: THEORY AND NUMERICAL COMPARISON OF STEP SIZE RULES∗

D. Lorenz, P. Maass, P. Q. Muoi, 2012 .

Quality Control and Characterization

Claas Falldorf, Michael Lütjen, Gert Goch, 2019, Lecture Notes in Production Engineering.

Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

Frank-Michael Schleif, Thomas Elssner, Peter Maaß, 2009 .

Exploring three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry data: three-dimensional spatial segmentation of mouse kidney.

Stefan Heldmann, Michael Becker, Theodore Alexandrov, 2012, Analytical chemistry.

Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer

Dennis Trede, Janina Oetjen, Peter Maass, 2019, Proteomics. Clinical applications.

Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation

Bart J. A. Mertens, Theodore Alexandrov, Herbert Thiele, 2009, Bioinform..

Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering.

Theodore Alexandrov, Herbert Thiele, Peter Maass, 2010, Journal of proteome research.

Cross-Normalization of MALDI Mass Spectrometry Imaging Data Improves Site-to-Site Reproducibility.

R. Casadonte, S. Deininger, J. Kriegsmann, 2021, Analytical chemistry.

Using the chemical noise background in MALDI mass spectrometry imaging for mass alignment and calibration.

J. H. Kobarg, R. Casadonte, J. Kriegsmann, 2019, Analytical chemistry.

Super-resolution segmentation of imaging mass spectrometry data: Solving the issue of low lateral resolution.

T. Alexandrov, S. Meding, D. Trede, 2011, Journal of proteomics.

Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma

Jean Le'Clerc Arrastia, T. Boskamp, P. Maass, 2021, J. Imaging.

Supervised non-negative matrix factorization methods for MALDI imaging applications

Pascal Fernsel, Peter Maass, Tobias Boskamp, 2018, Bioinform..

Numerical experiments with MALDI Imaging data

Pierre Vandergheynst, Mohammad Golbabaee, Chen Sagiv, 2014, Adv. Comput. Math..

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data

Theodore Alexandrov, Herbert Thiele, Dennis Trede, 2012, J. Integr. Bioinform..

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data

J. H. Kobarg, J. Oetjen, D. Trede, 2012, Journal of integrative bioinformatics.

A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data

Sergey I. Nikolenko, Theodore Alexandrov, Peter Maass, 2012, GCB.

Prediction of Temperature Induced Shape Deviations in dry Milling

Berend Denkena, Jost Vehmeyer, Peter Maaß, 2015 .

On the Connection Between Adversarial Robustness and Saliency Map Interpretability pdf

Carola-Bibiane Schönlieb, Christian Etmann, Peter Maass, 2019, ICML.

Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering pdf

Pascal Fernsel, Peter Maass, P. Maass, 2021, ArXiv.

Delay-range-dependent exponential H∞ synchronization of a class of delayed neural networks

Hamid Reza Karimi, Peter Maass, P. Maass, 2009 .

LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction

P. Maass, Maximilian Schmidt, Johannes Leuschner, 2021, Scientific data.

Adaptive wavelet methods and sparsity reconstruction for inverse heat conduction problems

Stephan Dahlke, Peter Maass, Thorsten Raasch, 2010, Adv. Comput. Math..

Two-step regularization methods for linear inverse problems

P. Maaß, R. Ramlau, P. Maass, 2006 .

Adaptive Finite Elements and Mathematical Optimization Methods

Carsten Carstensen, Andreas Rademacher, Heribert Blum, 2013 .

PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization

G. Steidl, P. Maass, Alexander Denker, 2022, ArXiv.

A Deep Prior Approach to Magnetic Particle Imaging

Tobias Kluth, Peter Maass, Sören Dittmer, 2020, MLMIR@MICCAI.

Error analysis for filtered back projection reconstructions in Besov spaces pdf

Peter Maass, Matthias Beckmann, Judith Nickel, 2020, Inverse Problems.

Extraction and Analysis of Structural Features in Cloud Radar and Lidar Data Using Wavelet Based Methods

G. Teschke, P. Maass, M. Zhariy, 2002 .

The x-ray transform: singular value decomposition and resolution

Peter Maass, P. Maass, 1987 .

Deep learning methods for partial differential equations and related parameter identification problems

P. Maass, U. Iben, A. Rademacher, 2022, Inverse Problems.

Is Deep Image Prior in Need of a Good Education? pdf

Bangti Jin, Peter Maass, Riccardo Barbano, 2021, ArXiv.

A generalized conditional gradient method and its connection to an iterative shrinkage method

Dirk A. Lorenz, Kristian Bredies, Peter Maass, 2009, Comput. Optim. Appl..

A fast algorithm to find optimal controls of multiantenna applicators in regional hyperthermia.

P Wust, T Köhler, P Maass, 2001, Physics in medicine and biology.

SELTO: Sample-Efficient Learned Topology Optimization

P. Maass, Sören Dittmer, David Erzmann, 2022, ArXiv.

Improvement of Environment Observing Remote Sensing Devices by Regularization Techniques

P. Maaß, Christine Böckmann, Alexander Mekler, 2003 .

Deep learning detection of melanoma metastases in lymph nodes

Jean Le'Clerc Arrastia, D. Schadendorf, P. Jansen, 2023, European Journal of Cancer.

Special issue on linear algebra in signal and image processing - Preface

Sabine Van Huffel, Per Christian Hansen, P Maass, 2004 .

Magnification of Label Maps With a Topology-Preserving Level-Set Method

Chen Sagiv, Theodore Alexandrov, Dennis Trede, 2012, IEEE Transactions on Image Processing.

Tomographic methods for 2D reconstruction with the double crystal diffractometer

Peter Maass, W. Treimer, U. Feye-Treimer, 1992, IMPACT Comput. Sci. Eng..

EEG Signal Analysis by Continuous Wavelet Transform Techniques

Peter Maass, Alfred K. Louis, Gottfried Mayer-Kress, 1998 .

Modeling the influence of unbalances for ultra‐precision cutting processes

Ronny Ramlau, Peter Maass, Christina Brandt, 2011 .

Sparsity reconstruction in electrical impedance tomography: An experimental evaluation

Jari P. Kaipio, Bangti Jin, Tobias Kluth, 2012, J. Comput. Appl. Math..

A reconstruction algorithm for electrical impedance tomography based on sparsity regularization

Bangti Jin, Taufiquar Khan, Peter Maass, 2012 .

Joint super-resolution image reconstruction and parameter identification in imaging operator: analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging pdf

Tobias Kluth, Ming Jiang, Peter Maass, 2020, Inverse Problems.

Conditional Invertible Neural Networks for Medical Imaging

Peter Maass, Johannes Leuschner, Alexander Denker, 2021, J. Imaging.

Fast CG-Based Methods for Tikhonov-Phillips Regularization

Andreas Frommer, Peter Maass, A. Frommer, 1999, SIAM J. Sci. Comput..

Signal representation, uncertainty principles and localization measures

Bruno Torrésani, Chen Sagiv, Peter Maass, 2014, Adv. Comput. Math..

Method and apparatus for computer-assisted processing of a digitized image as well as machine-readable data carrier

Theodore Alexandrov, Peter Maass, Dennis Trede, 2011 .

Topology‐Preserving Geodesic Active Contours for Segmentation of High Content Fluorescent Cellular Images

D. Lorenz, D. Trede, P. Maass, 2008 .

Identification of the specific cutting force for geometrically defined cutting edges and varying cutting conditions

Berend Denkena, Jost Vehmeyer, Peter Maaß, 2014 .

Mathematical methods in signal processing and digital image analysis

Rainer Dahlhaus, Jürgen Kurths, Jens Timmer, 2008 .

Mathematical model of micro turning process

Hamid Reza Karimi, Peter Maass, Christina Brandt, 2009 .

Detection and classification of material attributes-a practical application of wavelet analysis

Gerd Teschke, Peter Maass, Werner Willmann, 2000, IEEE Trans. Signal Process..

Error analysis for filtered back projection reconstructions in Besov spaces

P. Maass, Matthias Beckmann, Judith Nickel, 2020, Inverse Problems.

SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction

P. Maass, Johannes Leuschner, Bangti Jin, 2023, ArXiv.

Singular Values for ReLU Layers

Emily J. King, Peter Maass, Sören Dittmer, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Regularity of the Parameter-to-State Map of a Parabolic Partial Differential Equation

P. Maass, S. Dahlke, Rudolf Ressel, 2014 .

Shrinkage versus deconvolution

Dirk A. Lorenz, Herbert Thiele, Peter Maass, 2007 .

Score-Based Generative Models for PET Image Reconstruction

S. Arridge, P. Maass, Alexander Denker, 2023, ArXiv.

A robust estimation method for camera calibration with known rotation

Chen Sagiv, Peter Maass, Amir Egozi, 2015 .

Deep Learning and Inverse Problems

Simon R. Arridge, Maarten V. de Hoop, Michael Unser, 2019 .

An Optimal Control Problem in Medical Image Processing

Dirk A. Lorenz, Kristian Bredies, Peter Maass, 2005, Systems, Control, Modeling and Optimization.

Wavelet versus JPEG (Joint Photographic Expert Group) and fractal compression. Impact on the detection of low-contrast details in computed radiographs.

T Liebig, J Ricke, H Amthauer, 1998, Investigative radiology.

Wideband radar: the hyp transform

Peter Maass, P. Maass, 1989 .

Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems

J. C. Ye, P. Maass, Alexander Denker, 2023, ArXiv.

Invertible residual networks in the context of regularization theory for linear inverse problems

P. Maass, Alexander Denker, Sören Dittmer, 2023, Inverse Problems.

A Projectional Ansatz to Reconstruction pdf

Peter Maass, Soren Dittmer, P. Maass, 2019, ArXiv.

Mathematical models for surface characterization of machining processes

Oltmann Riemer, Peter Maass, Bernd Kuhfuß, 2008 .

The interior Radon transform

Peter Maass, P. Maass, 1992 .

A convex optimisation approach to robust observer-based H∞ control design of linear parameter-varying delayed systems

Hamid Reza Karimi, Peter Maass, P. Maass, 2008, Int. J. Model. Identif. Control..

Image Denoising and Quality Inspection of Micro Components Using Perona-Malik Diffusion☆

Bernd Scholz-Reiter, Iwona Piotrowska-Kurczewski, Daniel Weimer, 2013 .