Deep Idempotent Network for Efficient Single Image Blind Deblurring
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33, (1), pp. 172-185
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Deep Idempotent Network for Efficient Single Image Blind Deblurring.pdf | Published version | 4.48 MB |
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In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multiview clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and infers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.
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