Multi-Modal Convolutional Dictionary Learning

Convolutional dictionary learning has become increasingly popular in signal and image processing for its ability to overcome the limitations of traditional patch-based dictionary learning. Although most studies on convolutional dictionary learning mainly focus on the unimodal case, real-world image...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 31; pp. 1325 - 1339
Main Authors: Gao, Fangyuan, Deng, Xin, Xu, Mai, Xu, Jingyi, Dragotti, Pier Luigi
Format: Journal Article
Language:English
Published: United States IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
convolutional sparse coding
Deep learning
Dictionaries
Fourier transforms
image denoising
Image processing
Infrared imagery
Machine learning
Memory management
Multi-modal dictionary learning
Noise level
Paints
Representations
Signal processing
Toy manufacturing industry
Training
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Convolutional dictionary learning has become increasingly popular in signal and image processing for its ability to overcome the limitations of traditional patch-based dictionary learning. Although most studies on convolutional dictionary learning mainly focus on the unimodal case, real-world image processing tasks usually involve images from multiple modalities, e.g., visible and near-infrared (NIR) images. Thus, it is necessary to explore convolutional dictionary learning across different modalities. In this paper, we propose a novel multi-modal convolutional dictionary learning algorithm, which efficiently correlates different image modalities and fully considers neighborhood information at the image level. In this model, each modality is represented by two convolutional dictionaries, in which one dictionary is for common feature representation and the other is for unique feature representation. The model is constrained by the requirement that the convolutional sparse representations (CSRs) for the common features should be the same across different modalities, considering that these images are captured from the same scene. We propose a new training method based on the alternating direction method of multipliers (ADMM) to alternatively learn the common and unique dictionaries in the discrete Fourier transform (DFT) domain. We show that our model converges in less than 20 iterations between the convolutional dictionary updating and the CSRs calculation. The effectiveness of the proposed dictionary learning algorithm is demonstrated on various multimodal image processing tasks, achieves better performance than both dictionary learning methods and deep learning based methods with limited training data.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2022.3141251