Proximal Minimization Methods with Generalized Bregman Functions
SIAM Journal on Control and Optimization
Convex analysis and variational problems
Convex analysis and variational problems
Mathematical methods in image reconstruction
Mathematical methods in image reconstruction
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Inverse Scale Space Theory for Inverse Problems
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
A Variational Approach to Reconstructing Images Corrupted by Poisson Noise
Journal of Mathematical Imaging and Vision
Error estimation for Bregman iterations and inverse scale space methods in image restoration
Computing - Special Issue on Industrial Geometry
SIAM Journal on Numerical Analysis
Bregman-EM-TV Methods with Application to Optical Nanoscopy
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A New Total Variation Method for Multiplicative Noise Removal
SIAM Journal on Imaging Sciences
Nonlinear Inverse Scale Space Methods for Total Variation Blind Deconvolution
SIAM Journal on Imaging Sciences
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
SIAM Journal on Imaging Sciences
Cahn-Hilliard Inpainting and a Generalization for Grayvalue Images
SIAM Journal on Imaging Sciences
Deconvolving Poissonian images by a novel hybrid variational model
Journal of Visual Communication and Image Representation
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Measurements in nanoscopic imaging suffer from blurring effects modeled with different point spread functions (PSF). Some apparatus even have PSFs that are locally dependent on phase shifts. Additionally, raw data are affected by Poisson noise resulting from laser sampling and "photon counts" in fluorescence microscopy. In these applications standard reconstruction methods (EM, filtered backprojection) deliver unsatisfactory and noisy results. Starting from a statistical modeling in terms of a MAP likelihood estimation we combine the iterative EM algorithm with total variation (TV) regularization techniques to make an efficient use of a-priori information. Typically, TV-based methods deliver reconstructed cartoon images suffering from contrast reduction. We propose extensions to EM-TV, based on Bregman iterations and primal and dual inverse scale space methods, in order to obtain improved imaging results by simultaneous contrast enhancement. Besides further generalizations of the primal and dual scale space methods in terms of general, convex variational regularization methods, we provide error estimates and convergence rates for exact and noisy data. We illustrate the performance of our techniques on synthetic and experimental biological data.