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Article Dans Une Revue IEEE Signal Processing Letters Année : 2022

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

Résumé

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 et versions

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

Identifiants

Citer

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, ⟨10.1109/lsp.2022.3155307⟩. ⟨hal-03381093⟩
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