Department of Mathematics
Non-Lipschitz lp-regularization and box constrained model for image restoration
Nonsmooth nonconvex regularization has remarkable advantages for the restoration of piecewise constant images. Constrained optimization can improve the image restoration using a priori information. In this paper, we study regularized nonsmooth nonconvex minimization with box constraints for image restoration. We present a computable positive constant θ for using nonconvex nonsmooth regularization, and show that the difference between each pixel and its four adjacent neighbors is either 0 or larger than θ in the recovered image. Moreover, we give an explicit form of θ for the box-constrained image restoration model with the non-Lipschitz nonconvex l p-norm (0
Box constraints, image restoration, non-Lipschitz, nonsmooth and nonconvex, regularization
Source Publication Title
IEEE Transactions on Image Processing
Institute of Electrical and Electronics Engineers
Chen, Xiaojun, Michael K. Ng, and Chao Zhang. "Non-Lipschitz lp-regularization and box constrained model for image restoration." IEEE Transactions on Image Processing 21.12 (2012): 4709-4721.