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Mixed noise reduction via sparse error constraint representation of high frequency image for wildlife image

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Abstract

Wildlife image noise reduction is a difficult and challenging problem since the images are inevitably corrupted by the mixed noise in the complex field environments. Most of the existing denoising methods focused on the noise removal added manually to pure images. However, the noise of wildlife images taken in the field is random mixed noise; thus, the existing noise reduction algorithms are not suitable for the denoising of wildlife images. In this paper, we propose a novel mixed noise reduction method based on sparse error constraint representation for removing the wildlife image noise. Firstly, we use 2D-DCT method to decompose a noisy image into a high frequency image and a low frequency image, and then rank 2D-DCT coefficients based on Zig-zag sorting algorithm. As we know, the high frequency image contains more noise, so the dictionary learning model of high frequency image is established to recover the images corrupted by the mixed noise. The sparse error term describes the error between the sparse coefficients of the original image and those obtained by the error constraint method. And then we utilize the algorithm proximal alternating linearization minimization to solve the objective function due to the nonconvex and non-smooth minimization problem. In order to update the dictionary, we apply the lp- l1- norm term for sparse coding to obtain the optimal solution of sparse coefficients. The experiment results show that the proposed method has good noise reduction results for both the noisy images recorded in the wild and the images artificially corrupted by mixed noise while retain more details of the wildlife objects in the restored images.

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Data availability statement

Experiments are performed using the wild animal images from wild-animal datasets (www.snapshotserengeti.org).

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Acknowledgments

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant. 31200496.

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Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, 210037, China

    Yuan Xu, Yaqin Zhao & Peng Lu

Authors
  1. Yuan Xu
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  2. Yaqin Zhao
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  3. Peng Lu
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Contributions

Conceptualization, Yuan Xu. and Yaqin Zhao.; methodology, Yuan Xu.; software, Yuan Xu.; validation, Yaqin Zhao. And Peng Lu.; formal analysis, Peng Lu.; investigation, Yuan Xu.; resources, Yaqin Zhao.; data curation, Peng Lu.; writing—original draft preparation, Yuan Xu.; writing—review and editing, Yaqin Zhao..; visualization, Yuan Xu.; supervision, Yaqin Zhao.; project administration, Yaqin Zhao.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yaqin Zhao.

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Xu, Y., Zhao, Y. & Lu, P. Mixed noise reduction via sparse error constraint representation of high frequency image for wildlife image. Multimed Tools Appl 81, 44045–44058 (2022). https://doi.org/10.1007/s11042-022-13247-7

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  • DOI: https://doi.org/10.1007/s11042-022-13247-7

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