Example-based single document image super-resolution: a global MAP approach with outlier rejection
Multidimensional Systems and Signal Processing
On the entropy of a hidden Markov process
Theoretical Computer Science
Fast Prototype Based Noise Reduction
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Universal source controlled channel decoding with nonsystematic quick-look-in turbo codes
IEEE Transactions on Communications
IEEE Transactions on Signal Processing
A context quantization approach to universal denoising
IEEE Transactions on Signal Processing
Universal estimation of erasure entropy
IEEE Transactions on Information Theory
On concentration for denoiser-loss estimators
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Discrete denoising with shifts
IEEE Transactions on Information Theory
Lower limits of discrete universal denoising
IEEE Transactions on Information Theory
Multi-layer filtering approach for map images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A universal scheme for Wyner-Ziv coding of discrete sources
IEEE Transactions on Information Theory
A linear encoding approach to index assignment in lossy source-channel coding
IEEE Transactions on Information Theory
On information divergence measures and a unified typicality
IEEE Transactions on Information Theory
The interplay between entropy and variational distance
IEEE Transactions on Information Theory
Least squares estimation without priors or supervision
Neural Computation
Fuzzy c-means clustering with weighted image patch for image segmentation
Applied Soft Computing
| Hi-index | 755.26 |
A discrete denoising algorithm estimates the input sequence to a discrete memoryless channel (DMC) based on the observation of the entire output sequence. For the case in which the DMC is known and the quality of the reconstruction is evaluated with a given single-letter fidelity criterion, we propose a discrete denoising algorithm that does not assume knowledge of statistical properties of the input sequence. Yet, the algorithm is universal in the sense of asymptotically performing as well as the optimum denoiser that knows the input sequence distribution, which is only assumed to be stationary. Moreover, the algorithm is universal also in a semi-stochastic setting, in which the input is an individual sequence, and the randomness is due solely to the channel noise. The proposed denoising algorithm is practical, requiring a linear number of register-level operations and sublinear working storage size relative to the input data length.