基于模糊均差的低照度图像平滑去噪方法研究
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电子科技大学成都学院,计算机学院

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中图分类号:

TP751

基金项目:

2021国腾创投教改项目:《云数据存储系统线上线下课程建设》(项目编号:GTJG-02)


Research on Smoothing Denoising Method of Low Illumination Image based on Fuzzy Mean Difference
Author:
Affiliation:

School of Computer Science, Chengdu College of University of Electronic Science and Technology of China

Fund Project:

2021 Guoteng venture capital education reform project: online and offline course construction of cloud data storage system (Project No: GTJG-02)

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    摘要:

    为了提高低照度图像去噪处理的整体效果,本次研究提出了基于模糊均差的低照度图像平滑去噪方法。采用Sobel梯度检测图像边缘信息,采用OTSU(Nobuyuki otsu 大津展之)阈值分割法分割图像为平坦区域和细节区域;通过模糊均差方法分别估计两个区域图像的噪声标准差;采用小波域方法对图像进行平滑软阈值去噪,实现低照度图像的平滑去噪。实验结果显示,本文方法可以在不同噪声水平下获取接近真实值噪声标准差,在噪声水平最大时信噪比大小达到了27.97dB,去噪效果达到92.1%,质量很好的图像数量占比达到了80.58%,图像信息损失较小,去噪效果较好,具有极大的应用价值。

    Abstract:

    In order to improve the overall effect of low illumination image denoising, a low illumination image smoothing denoising method based on fuzzy mean difference is proposed in this study. Sobel gradient is used to detect the image edge information, and low illumination images were segmented into flat and detail regions by OTSU (Nobuyuki otsu) threshold segmentation; The noise standard deviation of two regional images is estimated by fuzzy mean difference method; The wavelet domain method is used to smooth the soft threshold denoising of the image to realize the smooth denoising of the low illumination image. The experimental results show that this method can obtain the noise standard deviation close to the real value under different noise levels. When the noise level is the largest, the signal-to-noise ratio reaches 27.97db, the denoising effect reaches 92.1%, the proportion of the number of images with good quality reaches 80.58%, the image information loss is small, the denoising effect is good, and it has great application value.

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罗丹. 基于模糊均差的低照度图像平滑去噪方法研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-12-30
  • 最后修改日期:2022-03-28
  • 录用日期:2022-04-30
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