ZHONG Wen, LUO Qiqiang. Iterative Bilateral Median Filter Based on Intensity Features and Mode Principle[J]. Infrared Technology , 2023, 45(12): 1330-1336.
Citation: ZHONG Wen, LUO Qiqiang. Iterative Bilateral Median Filter Based on Intensity Features and Mode Principle[J]. Infrared Technology , 2023, 45(12): 1330-1336.

Iterative Bilateral Median Filter Based on Intensity Features and Mode Principle

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  • Received Date: September 13, 2022
  • Revised Date: February 27, 2023
  • In order to effectively maintain and restore the edges and details of infrared images while removing the impulse noise, an iterative bilateral median filter based on intensity features and mode principle is proposed. In this method, based on the intensity features of impulse noise and the mode principle, the pixels that take the minimum and maximum values and are isolated on the intensity distribution of the neighborhood are recognized as noisy pixels. According to the weighted coefficients with respect to the spatial distance and intensity similarity, the noiseless pixels in the neighborhood and the pixels that have been denoised and restored are weighted by the frequencies, and the frequency weighted median is used as the estimated value of noisy pixels. Furthermore, the denoising processing is performed in the way of iterative traversal processing, which makes the most of the results of the previous traversal processing to remove high density noise. The experimental data confirm that the PSNR and EPI values and the visual effects achieved by the proposed method are better than the existing methods, with better denoising performance.
  • [1]
    Khan S, Lee D. An adaptive dynamically weighted median filter for impulse noise removal[J]. EURASIP Journal on Advances In Signal Processing, 2017, 2017(1): 1-14. DOI: 10.1186/s13634-016-0440-1
    [2]
    Erkan U, Gökrem L. A new method based on pixel density in salt and pepper noise removal[J]. Turkish Journal of Electrical Engineering & Computer Sciences, 2018, 26(1): 162-171.
    [3]
    Balasubramanian G, Chilambuchelvan A, Vijayan S, et al. An extremely fast adaptive high performance filter to remove salt and pepper noise using overlapping medians in images[J]. Imaging Science Journal, 2016, 64(5): 241-252. DOI: 10.1080/13682199.2016.1168144
    [4]
    ZHANG Z, HAN D, Dezert J, et al. A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning[J]. Signal Processing, 2018, 147(2018): 173-189.
    [5]
    Erkan U, Serdar E, Thanh D, et al. Adaptive frequency median filter for the salt and pepper denoising problem[J]. IET Image Processing, 2020, 14(7): 1291-1302. DOI: 10.1049/iet-ipr.2019.0398
    [6]
    SHAO C, Kaur P, Kumar R. An improved adaptive weighted mean filtering approach for metallographic image processing[J]. Journal of Intelligent Systems, 2021, 30(1): 470-478. DOI: 10.1515/jisys-2020-0080
    [7]
    徐超, 冯辅周, 闵庆旭, 等. 基于形态学和OTSU算法的红外图像降噪及分割[J]. 红外技术, 2017, 39(6): 512-516. http://hwjs.nvir.cn/article/id/hwjs201706006

    XU C, FENG F, MIN Q, et al. Infrared image denoising and segmentation based on morphology and Otsu method[J]. Infrared Technology, 2017, 39(6): 512-516. http://hwjs.nvir.cn/article/id/hwjs201706006
    [8]
    LIU N, YANG C, CAO H. Noise suppression of the reconstruction of infrared digital holography based on pyramid-based bilateral filter[J]. Infrared Physics & Technology, 2017, 85: 352-358.
    [9]
    Goel N, Kaur H, Saxena. Modified decision based unsymmetric adaptive neighborhood trimmed mean filter for removal of very high density salt and pepper noise[J]. Multimedia Tools and Applications, 2020, 79: 19739-19768. DOI: 10.1007/s11042-020-08687-y
    [10]
    顾雅青, 葛宾, 高晨. 基于模糊滤波器的钢水红外图像混合噪声处理[J]. 红外技术, 2019, 41(7): 623-627. http://hwjs.nvir.cn/article/id/hwjs201907005

    GU Y, GE B, GAO C. Fuzzy filter-based mixed noise processing for molten steel infrared image[J]. Infrared Technology, 2019, 41(7): 623-627. http://hwjs.nvir.cn/article/id/hwjs201907005
    [11]
    Vasanth K, Ravi C, Nagaraj S, et al. A decision based asymmetrically trimmed modified geometric mean algorithm for the removal of high density salt and pepper noise in images and videos[J]. Smart Computing Techniques and Applications, 2021, 225(2021): 147-154.
    [12]
    Sharma N, Sohi P, Garg B, et al. A novel multilayer decision based iterative filter for removal of salt and pepper noise[J]. Multimedia Tools and Applications, 2021, 80(17): 26531-26545. DOI: 10.1007/s11042-021-10958-1
    [13]
    王加, 周永康, 李泽民, 等. 非制冷红外图像降噪算法综述[J]. 红外技术, 2021, 43(6): 557-565. http://hwjs.nvir.cn/article/id/380dcf6e-de3d-4411-ab70-e246d5c8ea27

    WANG J, ZHOU Y, LI Z, et al. A survey of uncooled infrared image denoising algorithms[J]. Infrared Technology, 2021, 43(6): 557-565. http://hwjs.nvir.cn/article/id/380dcf6e-de3d-4411-ab70-e246d5c8ea27
    [14]
    Enginoğlu S, Erkan U, Memiş S. Adaptive cesáro mean filter for salt-and-pepper noise removal[J]. El-Cezeri Journal of Science and Engineering, 2020, 7(1): 304-314.
    [15]
    CHEN J, ZHAN Y, CAO H. Adaptive sequentially weighted median filter for image highly corrupted by impulse noise [J]. IEEE Access, 2019, 7(2019): 158545-158556.
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