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This content will become publicly available on June 28, 2024

Title: Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise
In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by k -means clustering to segment the image. Specifically for the image smoothing step, we replace the least-squares fidelity for Gaussian noise in the Mumford-Shah model with a maximum posterior (MAP) term to deal with Poisson noise and we incorporate the weighted difference of anisotropic and isotropic total variation (AITV) as a regularization to promote the sparsity of image gradients. For such a nonconvex model, we develop a specific splitting scheme and utilize a proximal operator to apply the alternating direction method of multipliers (ADMM). Convergence analysis is provided to validate the efficacy of the ADMM scheme. Numerical experiments on various segmentation scenarios (grayscale/color and multiphase) showcase that our proposed method outperforms a number of segmentation methods, including the original SaT.  more » « less
Award ID(s):
2151235 1952644 1854434 1846690
NSF-PAR ID:
10440861
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Frontiers in Computer Science
Volume:
5
ISSN:
2624-9898
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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