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Found 5 papers, 4 papers with code
An Efficient Approximate Method for Online Convolutional Dictionary Learning
Most existing convolutional dictionary learning (CDL) algorithms are based on batch learning, where the dictionary filters and the convolutional sparse representations are optimized in an alternating manner using a training dataset.
Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion
Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports.
Efficient ADMM-based Algorithms for Convolutional Sparse Coding
Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model.
Coupled Feature Learning for Multimodal Medical Image Fusion
The resulting performance and execution times show the competitiveness of the proposed method in comparison with state-of-the-art medical image fusion methods.
Multi-Focus Image Fusion Using Sparse Representation and Coupled Dictionary Learning
In addition, to improve the fusion performance, we employ a coupled dictionary learning approach that enforces pairwise correlation between atoms of dictionaries learned to represent the focused and blurred feature spaces.
