Digital halftoning
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Color Halftoning
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Maximum A Posteriori by Annealed Mean Field Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
A Field Model for Human Detection and Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Solving Markov Random Fields using Second Order Cone Programming Relaxations
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Dense Photometric Stereo: A Markov Random Field Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating Optimal Parameters for MRF Stereo from a Single Image Pair
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Digital Halftoning, Second Edition
Modern Digital Halftoning, Second Edition
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate Labeling via Graph Cuts Based on Linear Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Steganalysis of halftone image using inverse halftoning
Signal Processing
Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
IEEE Transactions on Signal Processing
A robust technique for image descreening based on the wavelettransform
IEEE Transactions on Signal Processing
Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference
IEEE Transactions on Signal Processing
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Recursive structure of noncausal Gauss-Markov random fields
IEEE Transactions on Information Theory - Part 2
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inverse halftoning via MAP estimation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
An adaptive inverse halftoning algorithm
IEEE Transactions on Image Processing
Lagrangian-based methods for finding MAP solutions for MRF models
IEEE Transactions on Image Processing
Hybrid LMS-MMSE inverse halftoning technique
IEEE Transactions on Image Processing
Look-up table (LUT) method for inverse halftoning
IEEE Transactions on Image Processing
Tree-structured method for LUT inverse halftoning and for image halftoning
IEEE Transactions on Image Processing
Inverse halftoning algorithm using edge-based lookup table approach
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution
IEEE Transactions on Image Processing
Phase Unwrapping via Graph Cuts
IEEE Transactions on Image Processing
Combining Monte Carlo and Mean-Field-Like Methods for Inference in Hidden Markov Random Fields
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Background Removal of Multiview Images by Learning Shape Priors
IEEE Transactions on Image Processing
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
IEEE Transactions on Image Processing
Filters involving derivatives with application to reconstruction from scanned halftone images
IEEE Transactions on Image Processing
Inverse halftoning using binary permutation filters
IEEE Transactions on Image Processing
| Hi-index | 0.08 |
Inverse dithering is to restore the original continuous-tone image from its dithering halftone. We propose to use iterated conditional modes (ICM) for approximating a maximum a posteriori (MAP) solution to the inverse problem. The statistical model on which the ICM is based takes advantage of the information on dither arrays. For the considered two common MRF's for measuring the smoothness of images, the corresponding energy functions are convex. The combination of this convexity and the structure of the constraint space associated with the MAP problem guarantees the global optimality. The ICM always searches the valid image space for a better estimate. There is no question of going beyond the valid space. In addition, it requires only local computation and is easy to implement. The experimental results show that the restored images have high quality. Compared with two previous DMI (dithering-model based inverse) methods, our ICM has higher PSNR's by 0.5-1.3dB. The results also show that using the Gauss MRF (GMRF) for the continuous-tone image often had higher PSNR than using the Huber MRF (HMRF). An advantage of the GMRF is that it makes the ICM much easier to implement than the HMRF makes.