Abstract
In this paper, a new relaxation model based on mean curvature for adaptive image restoration is proposed. To solve the problem efficiently, an alternating direction method of multipliers (ADMMs) is proposed. Furthermore, a rigorous convergence theory of the proposed algorithm is established. We also give the complexity analysis of our proposed method. Experimental results are provided to demonstrate the effectiveness and efficiency of the proposed method over a state-of-the-art method on synthetic and natural images.
Highlights
Image restoration has always been a research hotspot in the field of image processing
It mainly includes total variation (TV) regularization [1,2,3], nonlocal regularization [4], sparse regularization [5], higher-order regularization based on higher-order derivatives [6,7,8,9], and fractionalorder regularization based on fractional-order derivatives [10, 11]
We have proposed a new relaxation model based on mean curvature for adaptive image denoising
Summary
Image restoration has always been a research hotspot in the field of image processing To solve this inverse problem, one of the most popular research directions is applying variational regularization methods. Due to the high nonlinearity and nonconvexity of its regularization term, the relaxation form of model (2) was studied They proposed a spatially adaptive hybrid regularization-based minimization problem, which can be given as min α(x, y)|∇u| + β(x, y)∇2udxdy + 1 (u − f)2dx dy, uΩ. For solving this relaxation model, the ADMM-based numerical algorithm was presented and its convergence was Mathematical Problems in Engineering proved [8, 35].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
