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A Modified Anisotropic Diffusion Scheme for Signal-Dependent Noise Filtering

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Abstract

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No. R-2023-577.

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Authors and Affiliations

  1. Faculty of Sciences, Electronics and Microelectronics Laboratory, University of Monastir, 5019, Monastir, Tunisia

    Mariem Ben Abdallah, Jihene Malek & Hafedh Belmabrouk

  2. Department of Computer Science and Information, College of Science, Majmaah University, 11932, Al-Majmaah, Saudi Arabia

    Abdullah Bajahzar

  3. Department of Physics, College of Science, Majmaah University, 11932, Al-Majmaah, Saudi Arabia

    Hafedh Belmabrouk

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  1. Mariem Ben Abdallah
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Correspondence to Abdullah Bajahzar or Hafedh Belmabrouk.

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Ben Abdallah, M., Malek, J., Bajahzar, A. et al. A Modified Anisotropic Diffusion Scheme for Signal-Dependent Noise Filtering. Circuits Syst Signal Process 43, 2184–2223 (2024). https://doi.org/10.1007/s00034-023-02538-5

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