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A Bimodal Co-Sparse Analysis Model for Image Processing
In this paper, we propose a co-sparse analysis model that is able to capture the interdependency of two image modalities.
Co-Sparse Textural Similarity for Image Segmentation
We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation within a convex multilabel optimization framework.
Separable Dictionary Learning
Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary.
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene.
Analysis Operator Learning and Its Application to Image Reconstruction
Our method is based on an $\ell_p$-norm minimization on the set of full rank matrices with normalized columns.
