Manifold models for signals and images
Computer Vision and Image Understanding
Morphological Diversity and Sparsity for Multichannel Data Restoration
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Locally Parallel Textures Modeling with Adapted Hilbert Spaces
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A proximal iteration for deconvolving Poisson noisy images using sparse representations
IEEE Transactions on Image Processing
Text extraction from graphical document images using sparse representation
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
An overview of inverse problem regularization using sparsity
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Monotone operator splitting for optimization problems in sparse recovery
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image inpainting by patch propagation using patch sparsity
IEEE Transactions on Image Processing
Uniform discrete curvelet transform
IEEE Transactions on Signal Processing
A comprehensive framework for image inpainting
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Super-resolution with sparse mixing estimators
IEEE Transactions on Image Processing
Exploiting redundancy for aerial image fusion using convex optimization
Proceedings of the 32nd DAGM conference on Pattern recognition
Spatial error concealment with sequence-aligned texture modeling and adaptive directional recovery
Journal of Visual Communication and Image Representation
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Learning the Morphological Diversity
SIAM Journal on Imaging Sciences
3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform
Journal of Mathematical Imaging and Vision
On the application of structured sparse model selection to JPEG compressed images
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Wavelet frame based surface reconstruction from unorganized points
Journal of Computational Physics
Locally Parallel Texture Modeling
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences
Linear inverse problems with various noise models and mixed regularizations
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Removal of random-valued impulse noise using overcomplete DCT dictionary
Proceedings of the CUBE International Information Technology Conference
Repairing sparse low-rank texture
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
An Efficient Algorithm for l0 Minimization in Wavelet Frame Based Image Restoration
Journal of Scientific Computing
Dictionary learning for image prediction
Journal of Visual Communication and Image Representation
Pattern Recognition Letters
Non-negative sparse decomposition based on constrained smoothed ℓ0 norm
Signal Processing
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Representing the image to be inpainted in an appropriate sparse representation dictionary, and combining elements from Bayesian statistics and modern harmonic analysis, we introduce an expectation maximization (EM) algorithm for image inpainting and interpolation. From a statistical point of view, the inpainting/interpolation can be viewed as an estimation problem with missing data. Toward this goal, we propose the idea of using the EM mechanism in a Bayesian framework, where a sparsity promoting prior penalty is imposed on the reconstructed coefficients. The EM framework gives a principled way to establish formally the idea that missing samples can be recovered/interpolated based on sparse representations. We first introduce an easy and efficient sparse-representation-based iterative algorithm for image inpainting. Additionally, we derive its theoretical convergence properties. Compared to its competitors, this algorithm allows a high degree of flexibility to recover different structural components in the image (piecewise smooth, curvilinear, texture, etc.). We also suggest some guidelines to automatically tune the regularization parameter.