An Improved Infrared Denoising Algorithm Based on Propagation Filter

Due to the influence of material limitation, immature process and environmental factors, the infrared image has the problems of poor contrast and low signal-to-noise ratio, which reduces the quality of infrared image and affects the ability to extract useful information from infrared images. In order to solve this problem, the traditional propagation filter is improved in this paper. The propagation mode is perfected to eight-neighborhood, and the method to judge the propagation along the inclined direction is added. The method of nonlinear transformation can better protect the edge information of infrared image, suppress isolated noise points and improve the quality of infrared image. Experimental results show that the proposed algorithm improves both subjective vision and objective evaluation.

關鍵字

Infrared image ； Denoising ； Marginal propagation ； Eight-neighborhood

參考文獻

Y. Shen, Y. Chen, Q. Liu, S.Q. Lou, et al. Improved Anscombe transformation and total variation for denoising of lowlight infrared images[J]. Infrared Physics and Technology, 2018,93.

Kuppusamy P G, Jayaraman S, Joseph J. A customized nonlocal restoration scheme with adaptive strength of smoothening for magnetic resonance images[J]. Biomedical Signal Processing and Control, 2019, 49: 160-172.

Binbin, Y. An improved infrared image processing method based on adaptive threshold denoising. [J]. J Image Video Proc.2019,5 (2019).

Jingyu Y, Xue Z, Huanjing Y, et al. IBM3D: Integer BM3D for Efficient Image Denoising[J]. Circuits, Systems, and Signal Processing, 2018.

Zeng M, Chen Z. SOSO Boosting of the K-SVD Denoising Algorithm for Enhancing Fault-Induced Impulse Responses of Rolling Element Bearings[J]. IEEE Transactions on Industrial Electronics, 2019.

延伸閱讀

Dagher, I., & Taleb, C. (2014). Image Denoising Using Fourth Order Wiener Filter with Wavelet Quadtree Decomposition. Journal of Electrical and Computer Engineering, (2014), 7-15. https://doi.org/10.1155/2014/108393
Sharma, S., & Kalra, G. S. (2014). An Image Denoising Framework with Multi-resolution Bilateral Filtering and Normal Shrink Approach. Research Journal of Applied Sciences, Engineering and Technology, 7(6), 1240-1246. https://www.airitilibrary.com/Article/Detail?DocID=20407467-201412-201506300024-201506300024-1240-1246
Sun, X., He, N., Zhang, Y. Q., Zhen, X. Y., Lu, K., & Zhou, X. L. (2017). Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold. Applied Computational Intelligence and Soft Computing, 2017(), 109-119. https://doi.org/10.1155/2017/5835020
Fu, H., & Li, H. (2020). Research and Implementation of Image Denoising Algorithm Based on Deep Learning. Frontiers in Economics and Management, 1(9), 92-105. https://doi.org/10.6981/FEM.202009_1(9).0014
Dai, J. P., Luo, Z. Q., & Li, C. J. (2021). Infrared And Visible Image Fusion Based on Rolling Guidance Filter Combined with Convolutional Neural Network. 電腦學刊, 32(6), 52-65. https://doi.org/10.53106/199115992021123206005

國際替代計量

An Improved Infrared Denoising Algorithm Based on Propagation Filter

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