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.

翻译：我们提出了一种新的图像去噪模型，该模型基于具有自适应权重的双相型变增长全变差正则化。该模型旨在减少经典Rudin--Osher--Fatemi模型产生的阶梯效应，同时以类似方式保持图像的边缘。我们实现了该模型，并在一维和二维的合成图像与自然图像上，针对一系列噪声水平测试了其性能。