Proximal Based Strategies for Solving Discrete Mumford-Shah With Ambrosio-Tortorelli Penalization on Edges - Archive ouverte HAL Access content directly
Journal Articles IEEE Signal Processing Letters Year : 2022

Proximal Based Strategies for Solving Discrete Mumford-Shah With Ambrosio-Tortorelli Penalization on Edges

Abstract

This work is dedicated to joint image restoration and contour detection considering the Ambrosio-Tortorelli functional. Two proximal alternating minimization schemes with convergence guarantees are provided, PALM-AT and SL-PAM-AT, as well as closed-form expressions of the involved proximity operators. A thorough numerical study is conducted in order to evaluate the performance of both numerical schemes as well as comparisons to a more standard ℓ 1-based discrete Mumford-Shah functional.
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Dates and versions

hal-03381093 , version 1 (15-10-2021)

Identifiers

  • HAL Id : hal-03381093 , version 1

Cite

Hoang T V Le, Marion Foare, Nelly Pustelnik. Proximal Based Strategies for Solving Discrete Mumford-Shah With Ambrosio-Tortorelli Penalization on Edges. IEEE Signal Processing Letters, 2022. ⟨hal-03381093⟩
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