{"ID":2839345,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15060","arxiv_id":"2511.15060","title":"Image Denoising Using Transformed L1 (TL1) Regularization via ADMM","abstract":"Total variation (TV) regularization is a classical tool for image denoising, but its convex $\\ell_1$ formulation often leads to staircase artifacts and loss of contrast. To address these issues, we introduce the Transformed $\\ell_1$ (TL1) regularizer applied to image gradients. In particular, we develop a TL1-regularized denoising model and solve it using the Alternating Direction Method of Multipliers (ADMM), featuring a closed-form TL1 proximal operator and an FFT-based image update under periodic boundary conditions. Experimental results demonstrate that our approach achieves superior denoising performance, effectively suppressing noise while preserving edges and enhancing image contrast.","short_abstract":"Total variation (TV) regularization is a classical tool for image denoising, but its convex $\\ell_1$ formulation often leads to staircase artifacts and loss of contrast. To address these issues, we introduce the Transformed $\\ell_1$ (TL1) regularizer applied to image gradients. In particular, we develop a TL1-regulariz...","url_abs":"https://arxiv.org/abs/2511.15060","url_pdf":"https://arxiv.org/pdf/2511.15060v1","authors":"[\"Nabiha Choudhury\",\"Jianqing Jia\",\"Yifei Lou\"]","published":"2025-11-19T03:06:03Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"math.OC\"]","methods":"[]","has_code":false}