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Multiplicative Noise Removal via a Learned Dictionary
- Source :
- IEEE Transactions on Image Processing, IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2012, 21 (11), pp.4534-4543. 〈10.1109/TIP.2012.2205007〉, IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2012, 21 (11), pp.4534-4543. ⟨10.1109/TIP.2012.2205007⟩
- Publication Year :
- 2012
- Publisher :
- HAL CCSD, 2012.
-
Abstract
- International audience; Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.
- Subjects :
- Denoising
multiplicative noise
Logarithm
business.industry
Noise reduction
Multiplicative function
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Pattern recognition
Sparse approximation
Non-local means
Computer Graphics and Computer-Aided Design
Multiplicative noise
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Computer Science::Computer Vision and Pattern Recognition
[ INFO.INFO-TI ] Computer Science [cs]/Image Processing
Maximum a posteriori estimation
Artificial intelligence
business
sparse representation
Software
Mathematics
dictionary
variational model
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Image Processing, IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2012, 21 (11), pp.4534-4543. 〈10.1109/TIP.2012.2205007〉, IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2012, 21 (11), pp.4534-4543. ⟨10.1109/TIP.2012.2205007⟩
- Accession number :
- edsair.doi.dedup.....78646d50291b68348eb5006958dc1b9a