Collaborating filtering using unsupervised learning for image reconstruction from missing data
Abstract In the image acquisition process, important information in an image can be lost due to noise, occlusion, or even faulty image sensors. Therefore, we often have images with missing and/or corrupted pixels. In this work, we address the problem of image completion using a matrix completion app...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2018-11-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13634-018-0591-3 |