Abstract
It is widely known that the total variation regularization model preserves the edges well in the restored images but has some staircase effects. We consider using non-convex high-order total variation and overlapping group sparsity as a hybrid regularization to present a new denoising model. The proposed model can well preserve edges and reduce the staircase effect in the smooth region of the restored images. In order to solve the proposed hybrid model, we develop an efficient alternating minimization method. Compared with other models for removing Cauchy noise, numerical experimental results demonstrate that the superiority of the proposed model and algorithm, both in terms of visual and quantitative measures.
Highlights
Image restoration mainly includes image denoising and deblurring, which is an important problem in image processing
Motivated by the above studies, we propose the following hybrid model combining non-convex high-order total variation (HTV) and overlapping group sparsity (OGS)-total variation (TV) regularization for removing Cauchy noise: α arg min u2 log γ 2 + (u − f
The main contributions of this paper are as follows:(1) We first combine non-convex HTV and OGS-TV regularization together in Cauchy noise removal model. This hybrid regularization model is beneficial to improve the OGS-TV sparsity and effectively remove the Cauchy noise in the degraded image. (2)Using the technique of variable substitution, we develop a new algorithm under the framework of alternating direction method of multipliers (ADMM) to solve the proposed model. (3)We conduct extensive experiments to demonstrate that our proposed method has superior performance over the state-of-the-art methods in Cauchy denoising
Summary
Image restoration mainly includes image denoising and deblurring, which is an important problem in image processing. Where, φ(∇u) is the OGS-TV regular function They utilized the alternating direction method of multiplies(ADMM) and majorization minimization(MM) method to propose an efficient algorithm to solve the model (3). Motivated by the above studies, we propose the following hybrid model combining non-convex HTV and OGS-TV regularization for removing Cauchy noise:. The main contributions of this paper are as follows:(1) We first combine non-convex HTV and OGS-TV regularization together in Cauchy noise removal model. This hybrid regularization model is beneficial to improve the OGS-TV sparsity and effectively remove the Cauchy noise in the degraded image.
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