A Variational Model for Image Classification and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
An Edge-Preserving Image Reconstruction Using Neural Network
Journal of Mathematical Imaging and Vision
Orthonormal Vector Sets Regularization with PDE's and Applications
International Journal of Computer Vision
Adaptive rest condition potentials: first and second order edge-preserving regularization
Computer Vision and Image Understanding
Adaptive Rest Condition Potentials: Second Order Edge-Preserving Regularization
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Surface recovery by self shading projection
Signal Processing
Variational Image Binarization and its Multi-Scale Realizations
Journal of Mathematical Imaging and Vision
Image Deblurring in the Presence of Impulsive Noise
International Journal of Computer Vision
Coupled anisotropic diffusion for image selective smoothing
Signal Processing
Multiframe selective information fusion for 'looking through the woods'
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Geometric Analysis of Particle Motion in a Vector Image Field
Journal of Mathematical Imaging and Vision
Exploiting light projection for shape from shading
Applied Numerical Mathematics - Applied scientific computing: Advances in grid generation, approximation and numerical modeling
Algorithmic Differentiation: Application to Variational Problems in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A Fast Scheme for Multiscale Signal Denoising
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Image Denoising Using Similarities in the Time-Scale Plane
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
A new method for parameter estimation of edge-preserving regularization in image restoration
Journal of Computational and Applied Mathematics
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Continuous glass patterns for painterly rendering
IEEE Transactions on Image Processing
Mumford-Shah regularizer with spatial coherence
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Document image analysis: issues, comparison of methods and remaining problems
Artificial Intelligence Review
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Color image deblurring with impulsive noise
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
From a modified ambrosio-tortorelli to a randomized part hierarchy tree
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Time-Scale Similarities for Robust Image De-noising
Journal of Mathematical Imaging and Vision
On a System of Adaptive Coupled PDEs for Image Restoration
Journal of Mathematical Imaging and Vision
From a Non-Local Ambrosio-Tortorelli Phase Field to a Randomized Part Hierarchy Tree
Journal of Mathematical Imaging and Vision
| Hi-index | 0.01 |
This paper deals with edge-preserving regularization for inverse problems in image processing. We first present a synthesis of the main results we have obtained in edge-preserving regularization by using a variational approach. We recall the model involving regularizing functions φ and we analyze the geometry-driven diffusion process of this model in the three-dimensional (3-D) case. Then a half-quadratic theorem is used to give a very simple reconstruction algorithm. After a critical analysis of this model, we propose another functional to minimize for edge-preserving reconstruction purposes. It results in solving two coupled partial differential equations (PDEs): one processes the intensity, the other the edges. We study the relationship with similar PDE systems in particular with the functional proposed by Ambrosio-Tortorelli (1990, 1992) in order to approach the Mumford-Shah (1989) functional developed in the segmentation application. Experimental results on synthetic and real images are presented