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D3Net: Joint Demosaicking, Deblurring and Deringing
- Source :
- ICPR
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Images acquired with standard digital cameras have Bayer patterns and suffer from lens blur. A demosaicking step is implemented in every digital camera, yet blur often remains unattended due to computational cost and instability of deblur-ring algorithms. Linear methods, which are computationally less demanding, produce ringing artifacts in deblurred images. Complex non-linear deblurring methods avoid artifacts, however their complexity imply offline application after camera demosaicking, which leads to sub-optimal performance. In this work, we propose a joint demosaicking deblurring and deringing network with a light-weight architecture inspired by the alternating direction method of multipliers. The proposed network has a transparent and clear interpretation compared to other black-box data driven approaches. We experimentally validate its superiority over state-of-the-art demosaicking methods with offline deblurring.
- Subjects :
- Deblurring
business.product_category
Demosaicing
business.industry
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020206 networking & telecommunications
02 engineering and technology
Ringing artifacts
Linear methods
Data-driven
Pattern recognition (psychology)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
business
Joint (audio engineering)
Digital camera
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 25th International Conference on Pattern Recognition (ICPR)
- Accession number :
- edsair.doi...........026ba02c55067d5de35afe9d2672589a