Difference of Anisotropic and Isotropic TV for Segmentation under Blur and Poisson Noise. (arXiv:2301.03393v3 [cs.CV] UPDATED)

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 …

aim arxiv clustering difference framework image noise paper segmentation solution thresholding