Joint image denoising with gradient direction and edge-preserving regularization
作者：
Highlights：
• A new gradient-direction-based method is proposed to avoid the denoised edges to be blurred especially when the edges of the guidance image are weak or inexistent.
• The reconstructed gradient vectors are used for the purpose of making the guidance image deeply participate in the model optimization process.
• A specifically designed optimization procedure is proposed to solve these nonconvex subproblems.
• A new regularization term is formulated to weaken the effects of the unreliable prior information from the guidance image.
• Experimental results on public datasets and from benchmark methods consistently demonstrate the effectiveness of the proposed method both visually and quantitatively.
摘要
•A new gradient-direction-based method is proposed to avoid the denoised edges to be blurred especially when the edges of the guidance image are weak or inexistent.•The reconstructed gradient vectors are used for the purpose of making the guidance image deeply participate in the model optimization process.•A specifically designed optimization procedure is proposed to solve these nonconvex subproblems.•A new regularization term is formulated to weaken the effects of the unreliable prior information from the guidance image.•Experimental results on public datasets and from benchmark methods consistently demonstrate the effectiveness of the proposed method both visually and quantitatively.
论文关键词：Joint image denoising,Gradient direction,Majorization minimization,Nonlinear optimization,Nonconvex optimization
论文评审过程：Received 17 March 2021, Revised 19 December 2021, Accepted 21 December 2021, Available online 25 December 2021, Version of Record 24 January 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108506
