Image Denoising Using Transformed L1 (TL1) Regularization via ADMM
This work improves image denoising for applications like medical imaging or photography, but it is incremental as it builds on existing regularization and optimization frameworks.
The paper tackled image denoising by introducing a Transformed L1 (TL1) regularizer to address staircase artifacts and contrast loss from traditional total variation methods, achieving superior performance in suppressing noise and preserving edges.
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.