ADMM算法在3D彩色图像恢复中的应用
Application of ADMM Algorithm in 3D Color Image Recovery
摘要: 本文讨论稀疏信号的恢复和分离问题。利用加权l1范数进行稀疏诱导,提出了加权l1范数极小化约束模型,利用ADMM算法进行求解。在适当地假设下证明了算法的收敛性。对带有盐噪声和椒盐噪声的3D彩色图像进行了数值实验,并与JP算法及YALL1算法进行了数值比对。实验结果的峰值信噪比PSNR和相对误差RelErr表明,我们的算法具有较好的恢复效果。
Abstract:
This paper discusses the recovery and demixing problem of sparse signals. We propose a weighted l1 norm minimization model. Then the ADMM algorithm is applied to this model. We also prove the convergence property of our algorithm under mild conditions. At last, we do two numerical experiments for 3D color image, in which salt noise and salt-and-pepper noise are chosen. In addition, we compare our algorithm with two other algorithms: the JP and the YALL1. Numerical results of PSNR and RelErr show that our algorithm gives relatively better behavior.
参考文献
|
[1]
|
Jung, M. and Kang, M. (2015) Simultaneous Cartoon and Texture Image Restoration with Higher-Order Regularization. SIAM Journal on Imaging Sciences, 8, 721-756. [Google Scholar] [CrossRef]
|
|
[2]
|
He, L. and Wang, Y. (2014) Iterative Support Detection-Based Split Bregman Method for Wavelet Frame-Based Image Inpainting. IEEE Transactions on Image Processing, 23, 5470-5485. [Google Scholar] [CrossRef]
|
|
[3]
|
Mallat, S.Y.G. (2010) Super-Resolution with Sparse Mixing Estimators. IEEE Transactions on Image Processing, 19, 2889-2900. [Google Scholar] [CrossRef]
|
|
[4]
|
Arias, P., Facciolo, G., Caselles, V., et al. (2011) A Variational Framework for Exemplar-Based Image Inpainting. International Journal of Computer Vision, 93, 319-347. [Google Scholar] [CrossRef]
|
|
[5]
|
Wen, F., Adhikari, L., Pei, L., et al. (2017) Nonconvex Regularization Based Sparse Recovery and Demixing with Application to Color Image Inpainting. IEEE Access, 5, 11513-11527. [Google Scholar] [CrossRef]
|
|
[6]
|
Li, G.Y. and Pong, T.K. (2014) Global Convergence of Splitting Methods for Nonconvex Composite Optimization. SIAM Journal on Optimization, 25, 2434-2460. [Google Scholar] [CrossRef]
|
|
[7]
|
Studer, C. (2012) Recovery of Sparsely Corrupted Signals. IEEE Transactions on Information Theory, 58, 3115-3130. [Google Scholar] [CrossRef]
|
|
[8]
|
Yang, J. and Zhang, Y. (2011) Alternating Direction Algorithms for l1 Problems in Compressive Sensing. SIAM Journal on Scientific Computing, 33, 250-278. [Google Scholar] [CrossRef]
|