Adaptive double-phase Rudin--Osher--Fatemi denoising model
This is an incremental improvement for image processing applications.
The authors tackled the problem of image denoising by proposing a new model based on variable-growth total variation regularization with adaptive weight to reduce staircasing effects while preserving edges, and they tested it on synthetic and natural images across noise levels.
We propose a new image denoising model based on a variable-growth total variation regularization of double-phase type with adaptive weight. It is designed to reduce staircasing with respect to the classical Rudin--Osher--Fatemi model, while preserving the edges of the image in a similar fashion. We implement the model and test its performance on synthetic and natural images in 1D and 2D over a range of noise levels.