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Color image restoration using DSS-NL-mapping-based multi-noiseNet CNN model

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Abstract

Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, speckle noises, salt and pepper noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we propose a deep iterative down-up CNN for image denoising, which alternates between lowering and raising the determination of the feature maps. The proposed network performed on down-sampled sub-images, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing denoisers, the proposed network has several desirable properties, including (i) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network and (ii) the ability to remove spatially variant noise by specifying a non-uniform noise level map. In addition, train a network model for five different types of noises, and achieve better performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate the proposed model in comparison with state-of-the-art denoisers. The results show that the proposed model is effective and efficient, making it highly attractive for practical denoising applications.

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Data availability

The datasets generated and/or analyzed during the current study are available in Berkeley Segmentation Dataset 68, https://arxiv.org/ftp/arxiv/papers/2102/2102.09351.pd.

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Acknowledgements

The authors would like to acknowledge the Global Academy of Technology and Kalpataru Institute of Technology for providing facility to do this research work.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Global Academy of Technology, Bangalore, India

    M. P. Sundaresha & B. Gudi Anandthirtha

  2. Kalpataru Institute of Technology, Tiptur, 572201, Karnataka, India

    G. S. Nandeesh

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Contributions

Sundaresha M P and Anandthirtha B. Gudi conceived the idea and supervised the findings of this work. Nandeesh G S assisted with the language of the manuscript. All authors discussed the results and approved final manuscript.

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Correspondence to M. P. Sundaresha.

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Sundaresha, M.P., Anandthirtha, B.G. & Nandeesh, G.S. Color image restoration using DSS-NL-mapping-based multi-noiseNet CNN model. J Opt (2023). https://doi.org/10.1007/s12596-023-01375-8

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