DSD: document sparse-based denoising algorithm

Author:

Do T. H.,Ramos Terrades O.ORCID,Tabbone S.

Funder

Ministerio de Economía y Competitividad

H2020 European Research Council

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition

Reference43 articles.

1. Aharon M, Elad M, Bruckstein A (2006) K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. Sig Process 54(11):4311–4322

2. Barney E (2008) Modeling image degradations for improving OCR. In: Proceedings of the 16th European signal processing conference (EUSIPCO), pp 1–5

3. Candés EJ, Donoho DL (2000) Curvelets: a surprisingly effective nonadaptive representation for objects with edges. In: Rabut C, Cohen A, Schumaker L (eds) Curve and Surface Fitting: Saint-Malo 1999 (Innovations in Applied Mathematics), Vanderbilt University Press,  pp 105–120

4. Chatterjee P, Milanfar P (2010) Is denoising dead? Trans Image Process 19(4):895–911

5. Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20(1):33–61

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3. Weighted Basis Pursuit Denoising Algorithm and Its Application in Gear Fault Diagnosis;2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD);2021-10-21

4. EDNets: Deep Feature Learning for Document Image Classification Based on Multi-view Encoder-Decoder Neural Networks;Document Analysis and Recognition – ICDAR 2021;2021

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