Sub-pixel data fusion and edge-enhanced distance refinement for 2D/3D images
International Journal of Intelligent Systems Technologies and Applications
Morphological Diversity and Sparsity for Multichannel Data Restoration
Journal of Mathematical Imaging and Vision
Patch-based video processing: a variational Bayesian approach
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Image Processing
Mixture model-and least squares-based packet video error concealment
IEEE Transactions on Image Processing
Image inpainting by patch propagation using patch sparsity
IEEE Transactions on Image Processing
Geometrically Guided Exemplar-Based Inpainting
SIAM Journal on Imaging Sciences
Self-content super-resolution for ultra-HD up-sampling
Proceedings of the 9th European Conference on Visual Media Production
On analysis-based two-step interpolation methods for randomly sampled seismic data
Computers & Geosciences
Dictionary learning based impulse noise removal via L1-L1 minimization
Signal Processing
An efficient framework for image/video inpainting
Image Communication
Object removal and loss concealment using neighbor embedding methods
Image Communication
Pattern Recognition Letters
Computationally Efficient Formulation of Sparse Color Image Recovery in the JPEG Compressed Domain
Journal of Mathematical Imaging and Vision
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We combine the main ideas introduced in Part I with adaptive techniques to arrive at a powerful algorithm that estimates missing data in nonstationary signals. The proposed approach operates automatically based on a chosen linear transform that is expected to provide sparse decompositions over missing regions such that a portion of the transform coefficients over missing regions are zero or close to zero. Unlike prevalent algorithms, our method does not necessitate any complex preconditioning, segmentation, or edge detection steps, and it can be written as a progression of denoising operations. We show that constructing estimates based on nonlinear approximants is fundamentally a nonconvex problem and we propose a progressive algorithm that is designed to deal with this issue directly. The algorithm is applied to images through an extensive set of simulation examples, primarily on missing regions containing textures, edges, and other image features that are not readily handled by established estimation and recovery methods. We discuss the properties required of good transforms, and in conjunction, show the types of regions over which well-known transforms provide good predictors. We further discuss extensions of the algorithm where the utilized transforms are also chosen adaptively, where unpredictable signal components in the progressions are identified and not predicted, and where the prediction scenario is more general.