Deblurring Poissonian images by split Bregman techniques

By Simon Setzer, Gabriele Steidl, Tanja Teuber. Journal of Visual Communication and Image Representation, 2010

353 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/eb845414d006203318943f5bc19cc3ecaab6240b

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Citations in the corpus

Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization (2009, neutral): “, blurs), other non-additive and non-Gaussian noise models, such as Poissonian observations [19], [36], and other regularizer s, such as those based on frame representations.”
Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization (2012, background): “Here, we mention a few of such applications: image processing [13, 19, 35, 43], compressive sensing [55], SDP [47, 53] and statistics [22, 29, 48].”
Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems (2014, methodology): “This technique can be used to derive parallel forms of the Douglas-Rachford algorithm, namely the Parallel ProXimal Algorithm (PPXA) [65] and PPXA+ [66], as well as parallel versions of ADMM (Simultaneous Direction Method of Multipliers or SDMM) [67].”
Restoration of Poissonian Images Using Alternating Direction Optimization (2010, neutral): “In the experiments reported in [37], the TV denoising step of PIDAL-TV is implemented by an inner iterative algorithm with a tight stopping criterion based o n the change between two consecutive images.”
Proximal Splitting Methods in Signal Processing (2009, methodology): “This algorithm was derived from a slightly different viewpo int in [118] with a connection with the work of [71].”
This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice (2010, background): “This statistical simpliﬁcation is not without cost, as the linear projections of the scene must now be characterized as nonlinear observations.”