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Efficient ADMM-Based Algorithms for Convolutional Sparse Coding
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DOI：10.1109/LSP.2021.3135196.png)
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Abstract
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Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model. The most efficient convolutional sparse coding methods are based on the alternating direction method of multipliers and the convolution theorem. The only major difference between these methods is how they approach a convolutional least-squares fitting subproblem. In this letter, we present a novel solution for this subproblem, which improves the computational efficiency of the existing algorithms. The same approach is also used to develop an efficient dictionary learning method. In addition, we propose a novel algorithm for convolutional sparse coding with a constraint on the approximation error. Source codes for the proposed algorithms are available online.
Keywords:
Convolution
Signal processing algorithms
Dictionaries
Convolutional codes
Approximation algorithms
Optimization
Image coding
Convolutional sparse coding
constrained sparse approximation
dictionary learning
alternating direction method of multipliers
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IF:
9.6
Papers:
1.1W
Citations:
1.7W
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