M11. ​Image Restoration Under Poisson Noise 

Poisson noise is a very common noise statistic in imaging problems, specifically when data is obtained by photon counting. Poisson noise arises in a wide range of applications, such as phase retrieval, computed tomography (CT), fluorescence microscopy, and astronomical imaging. Inverse problems with Poisson noise, such as the recovery of an image from a blurred and noisy version, requires a regularization to model the underlying image as well as a data fidelity term taking into account the Poisson noise statistics.  Some popular regularizations for imaging applications include total variation (TV), fractional order TV (FOTV), wavelet thresholding, and low-rank models.
 
The data fidelity term for Poisson noise is well known to be the Kullback-Leibler divergence, which suffers from a nonlinearity, making it more complicated than the least-squares term for Gaussian noise. It can be avoided by applying a variance stabilizing transform that converts the Poissonian data into (approximately) Gaussian data, or by approximating the Poisson fidelity term by a weighted least-squares model. Otherwise, the nonlinearity causes difficulties in designing efficient algorithms and analyzing the convergence properties. Some optimization techniques used for Poisson noises include trust region algorithms, augmented Lagrangian methods, primal-dual majorization-minimization, and iteratively reweighted algorithms. In addition, recent advances in deep learning can be borrowed to deal with Poisson noise including neural networks, self-supervised denoising models, and dictionary learning. 
 
In this minisymposium, we aim to bring together a wide range of experts in this field to share ideas and findings in theoretical, numerical, and practical aspects. Topics include real-data applications, algorithmic approaches, the usage of neural networks for image restoration, Bayesian approaches, as well as semi-blind and blind problems. Of interest will also be mixed noise models, which occur in many practical applications. The objective is to have a thorough understanding of the behaviours and properties of Poisson noises and to brainstorm in-depth discussions on the numerical methods to effectively remove Poisson noise.


Organizers:
Federico Benvenuto, University of Genoa, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it.                                                                  
Yifei Lou, University of North Carolina at Chapel Hill, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.                                                   
Frank Werner, Julius-Maximilians-Universität Würzburg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.         

Invited Speakers:
Huibin Chang, Tianjin Normal University, P.R. China, This email address is being protected from spambots. You need JavaScript enabled to view it.
Bilinear decomposition based splitting algorithms for curvature driven image restoration

Germana Landi, University of Bologna, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.
The Balancing Principle for automatic restoration of Poissonian images

Alessandro Lanza, University of Bologna, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.

Jinglai Li, University of Birmingham, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems

Paolo Massa, Western Kentucky University, USA This email address is being protected from spambots. You need JavaScript enabled to view it.
Predictive risk estimation for inverse problems with Poisson data

Monica Pragliola, University of Naples Federico II, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.
Whiteness-based parameter selection for Poisson data in variational image processing

Zbigniew Szkutnik, AGH-University of Science and Technology, Poland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Weighted discrepancy principle and adaptivity in Poisson inverse problems

Tieyong Zeng, The Chinese University of Hong Kong, P.R. China, This email address is being protected from spambots. You need JavaScript enabled to view it.

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