Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Recovery of blocky images from noisy and blurred data
SIAM Journal on Applied Mathematics
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
A computational algorithm for minimizing total variation in image restoration
IEEE Transactions on Image Processing
Image restoration subject to a total variation constraint
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In this paper, we propose a general algorithm for image denoising when no a priori information on the noise is available. The image denoising problem is formulated as an inequality constrained minimization problem where the objective is a general convex regularization functional and the right-hand side of the constraint depends on the noise norm and is not known. The proposed method is an iterative procedure which, at each iteration, automatically computes both an approximation of the noise norm and an approximate solution of the minimization problem. Experimental results demonstrate the effectiveness of the proposed automatic denoising procedure.