期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 64, 期 2, 页码 417-431出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2015.2483480
关键词
Analysis dictionary learning; analysis model; SimCO; sparse representation
资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/K014307/1]
- MOD University Defence Research Collaboration in Signal Processing
- EPSRC [EP/K014307/2] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K014307/2, EP/K014307/1] Funding Source: researchfish
In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit l(2)-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms.
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