Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image restoration is a highly ill-posed problem and requires to be regularized. Many common image priors aim to make full use of natural image prior information. Total variation (TV) regularize prior has good performance of preserving edges but also has drawbacks in arising ,  propose a total variation based image restoration method using hyper-Laplacian prior for image gradient and the overlapping group sparsity prior for sparser image representation constraint.  adopt the alternating direction method of multipliers (ADMM) method to optimize the object function of the proposed model and discuss the parameter selection criterion in the complex formulation. Finally,  carry out experiments on various degrade images and compare our method with several classical state-of-the-art methods. Experimental results show that our method has good performance in convergence and suppressing staircase artifacts, which makes a good balance beten alleviating staircase effects and preserving image details. A total variation based image restoration method is proposed that using hyper-Laplacian prior for image gradient and the overlapping group sparsity prior for sparser image representation constraint.The alternating direction method of multipliers (ADMM) method is adopted to optimize the complex object function of the proposed model. Parameter selection criterions are discussed for the complex formulation.The method has good performance in convergence and suppressing staircase artifacts, which makes a good balance beten alleviating staircase effects and preserving image details.An image restoration method using hyper-Laplacian prior and overlapping group sparsity.
['Mingzhu Shi', 'Tingting Han', 'Shuaiqi Liu']
I am writing a research paper on a idea Total variation image restoration using hyper-Laplacian prior with overlapping group sparsity
[{"from": "human", "value": "I am writing a research paper on a idea Total variation image restoration using hyper-Laplacian prior with overlapping group sparsity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Image restoration is a highly ill-posed problem and requires to be regularized. Many common image priors aim to make full use of natural image prior information. Total variation (TV) regularize prior has good performance of preserving edges but also has drawbacks in arising ,  propose a total variation based image restoration method using hyper-Laplacian prior for image gradient and the overlapping group sparsity prior for sparser image representation constraint.  adopt the alternating direction method of multipliers (ADMM) method to optimize the object function of the proposed model and discuss the parameter selection criterion in the complex formulation. Finally,  carry out experiments on various degrade images and compare our method with several classical state-of-the-art methods. Experimental results show that our method has good performance in convergence and suppressing staircase artifacts, which makes a good balance beten alleviating staircase effects and preserving image details. A total variation based image restoration method is proposed that using hyper-Laplacian prior for image gradient and the overlapping group sparsity prior for sparser image representation constraint.The alternating direction method of multipliers (ADMM) method is adopted to optimize the complex object function of the proposed model. Parameter selection criterions are discussed for the complex formulation.The method has good performance in convergence and suppressing staircase artifacts, which makes a good balance beten alleviating staircase effects and preserving image details.An image restoration method using hyper-Laplacian prior and overlapping group sparsity."}]