Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Review of Nonlinear Diffusion Filtering
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Relations between Soft Wavelet Shrinkage and Total Variation Denoising
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Hidden Markov tree modeling of complex wavelet transforms
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Correspondences between wavelet shrinkage and nonlinear diffusion
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Digital Image Denoising Method with Edge Preservation Using Dyadic Lifting Schemes
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Nonlinear filtering for sparse signal recovery from incomplete measurements
IEEE Transactions on Signal Processing
A level-wavelet-dependent scheme for image denoising via undecimated wavelet transform
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
| Hi-index | 7.30 |
In this paper we consider a general setting for wavelet based image denoising methods. In fact, in both deterministic regularization methods and stochastic maximum a posteriori estimations, the denoised image f@^ is obtained by minimizing a functional, which is the sum of a data fidelity term and a regularization term that enforces a roughness penalty on the solution. The latter is usually defined as a sum of potentials, which are functions of a derivative of the image. By considering particular families of dyadic wavelets, we propose the use of new potential functions, which allows us to preserve and restore important image features, such as edges and smooth regions, during the wavelet denoising process. Numerical results are presented, showing the optimal performance of the denoising algorithm obtained.