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Discriminative feature representation for Noisy image quality assessment

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Abstract

Blind image quality assessment (BIQA) is one of the most challenging and difficult tasks in the field of IQA. Given that sparse representation through dictionary learning can learn the image feature well, this paper proposed a method termed Discriminative Feature Representation (DFR) from the perspective of feature learning for noise contaminated image quality assessment. DFR makes use of two sub-dictionaries composed of atoms featuring desirable image structures and undesirable noise, respectively. Noise is quantified via a joint evaluation of the sparse coefficients related to the atoms in the two sub-dictionaries. The method is validated using public databases with different types of noise, a comparison with other up-to-date methods is provided. The proposed method is also applied to CT images acquired at different-level doses and reconstructed by various well-known algorithms.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFA0704102, Grant 2017YFA0104302, Grant 2017YFC0109202 and Grant 2017YFC0107900, in part by the National High Technology Research and Devlopment Program of China (863 Program), under Grant 2015AA043203, in part by the National Natural Science Foundation under Grant 81827805, Grant 61801003, Grant 61871117, and Grant 81530060, in part by the Key Laboratory of Health Informatics, Chinese Academy of Sciences, in part by the Fundamental Research Funds for the Central Universities, in part by the Joint Research Project of Southeast University and Nanjing Medical University.

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  1. Yunbo Gu and Hui Tang contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Yunbo Gu, Hui Tang, Tianling Lv, Yang Chen, Zhiping Wang, Huazhong Shu & Limin Luo

  2. IETR, CNRS UMR 6164, INSA Rennes, 20 avenue des Buttes de Coesmes, 35708, Rennes, France

    Lu Zhang

  3. School of Optoelectronics, the Beijing Institute of Technology, Beijing, 100081, China

    Jian Yang

  4. Centre de Recherche en Information Biomedicalee Sino-Francais, 35042, Rennes, France

    Gouenou Coatrieux

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  1. Yunbo Gu
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Correspondence to Yang Chen.

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Gu, Y., Tang, H., Lv, T. et al. Discriminative feature representation for Noisy image quality assessment. Multimed Tools Appl 79, 7783–7809 (2020). https://doi.org/10.1007/s11042-019-08424-0

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