A Time‐Fractional Equation With the Gray Level Indicator for Image Multiplicative Noise Removal

ABSTRACT Synthetic aperture radar (SAR) images suffer from multiplicative noise, which significantly degrades their quality and visual effect. To address this issue, we propose a time‐fractional equation with a gray level indicator for SAR image denoising. By introducing the time‐fractional derivative, the model effectively interpolates between the heat and wave equations, preserving valuable information in highly oscillatory regions. Moreover, the fractional‐order derivative operator possesses nonlocal properties, allowing for the inclusion of information from nonlocal domains. In order to achieve better control over the diffusion process, we incorporate a gray level indicator into the diffusion coefficients of our model, which allows us to fully take into account the gray level information of the image. We also investigate the well‐posedness of the proposed model. Experiments on natural and real SAR images demonstrate the superiority of our method in removing multiplicative noise, particularly in highly oscillatory regions and texture‐rich images.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-05-19
DOI
https://doi.org/10.1002/mma.11033
Primary Topic
Image and Signal Denoising Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

A Time‐Fractional Equation With the Gray Level Indicator for Image Multiplicative Noise Removal

Zhongxiang Zhou, Xuejiao Bai, Wenjuan Yao, Jiebao Sun
Mathematical Methods in the Applied Sciences
Image and Signal Denoising Methods
article

A Time‐Fractional Equation With the Gray Level Indicator for Image Multiplicative Noise Removal

Zhongxiang Zhou, Xuejiao Bai, Wenjuan Yao, Jiebao Sun
article en

Abstract

ABSTRACT Synthetic aperture radar (SAR) images suffer from multiplicative noise, which significantly degrades their quality and visual effect. To address this issue, we propose a time‐fractional equation with a gray level indicator for SAR image denoising. By introducing the time‐fractional derivative, the model effectively interpolates between the heat and wave equations, preserving valuable information in highly oscillatory regions. Moreover, the fractional‐order derivative operator possesses nonlocal properties, allowing for the inclusion of information from nonlocal domains. In order to achieve better control over the diffusion process, we incorporate a gray level indicator into the diffusion coefficients of our model, which allows us to fully take into account the gray level information of the image. We also investigate the well‐posedness of the proposed model. Experiments on natural and real SAR images demonstrate the superiority of our method in removing multiplicative noise, particularly in highly oscillatory regions and texture‐rich images.

Mathematical Methods in the Applied SciencesVol. 48(12)
Harbin Institute of Technology (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Heilongjiang Province, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 91%
Image and Signal Denoising Methods
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