Visual reconstruction
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
A deterministic algorithm for reconstructing images with interacting discontinuities
CVGIP: Graphical Models and Image Processing
Regularized multichannel restoration using cross-validation
Graphical Models and Image Processing
Restoration of Archival Documents Using a Wavelet Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Algorithm for High-Resolution Color Image Reconstruction with Multisensors
NAA '00 Revised Papers from the Second International Conference on Numerical Analysis and Its Applications
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Multiscale Approach to Restoring Scanned Color Document Images with Show-Through Effects
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Analysis and recognition of highly degraded printed characters
International Journal on Document Analysis and Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Independent component analysis for document restoration
International Journal on Document Analysis and Recognition
Source separation in astrophysical maps using independent factor analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
International Journal on Document Analysis and Recognition
Blind separation of convolutive image mixtures
Neurocomputing
Low quality document image modeling and enhancement
International Journal on Document Analysis and Recognition
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
IEEE Transactions on Signal Processing
Multichannel blind deconvolution for source separation in convolutive mixtures of speech
IEEE Transactions on Audio, Speech, and Language Processing
A Novel Hybrid Model Framework to Blind Color Image Deconvolution
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Show-through cancellation in scans of duplex printed documents
IEEE Transactions on Image Processing
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
Blind Separation of Superimposed Shifted Images Using Parameterized Joint Diagonalization
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Journal of Intelligent Manufacturing
Hi-index | 0.01 |
In this paper, we apply Bayesian blind source separation (BSS) from noisy convolutive mixtures to jointly separate and restore source images degraded through unknown blur operators, and then linearly mixed. We found that this problem arises in several image processing applications, among which there are some interesting instances of degraded document analysis. In particular, the convolutive mixture model is proposed for describing multiple views of documents affected by the overlapping of two or more text patterns. We consider two different models, the interchannel model, where the data represent multispectral views of a single-sided document, and the intrachannel model, where the data are given by two sets of multispectral views of the recto and verso side of a document page. In both cases, the aim of the analysis is to recover clean maps of the main foreground text, but also the enhancement and extraction of other document features, such as faint or masked patterns. We adopt Bayesian estimation for all the unknowns and describe the typical local correlation within the individual source images through the use of suitable Gibbs priors, accounting also for well-behaved edges in the images. This a priori information is particularly suitable for the kind of objects depicted in the images treated, i.e., homogeneous texts in homogeneous background, and, as such, is capable to stabilize the ill-posed, inverse problem considered. The method is validated through numerical and real experiments that are representative of various real scenarios.