Infrared image enhancement algorithm based on adaptive weighted guided filter

doi:10.19682/j.cnki.1005-8885.2022.0003

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (2): 73-84.doi: 10.19682/j.cnki.1005-8885.2022.0003

• Image Processing • 上一篇下一篇

Infrared image enhancement algorithm based on adaptive weighted guided filter

卢莹¹,黄世奇¹,王文庆²,孙柯¹

1. 西安邮电大学
2. 西安邮电学院

收稿日期:2021-01-28 修回日期:2021-07-09 出版日期:2022-04-26 发布日期:2022-04-26
通讯作者: 王文庆 E-mail:wwq@xupt.edu.cn
基金资助:
国家自然科学基金项目

Infrared image enhancement algorithm based on adaptive weighted guided filter

Received:2021-01-28 Revised:2021-07-09 Online:2022-04-26 Published:2022-04-26
Supported by:
This work was supported by the National Natural Science Foundation of China (61673017, 61905285), and the Shaanxi
Provincial Department of Science and Technology Key Project in the Field of Industry (2018ZDXM-GY-039).

摘要/Abstract

摘要：

The physical principle of infrared imaging leads to the low contrast of the whole image, the blurring of contour and edge details, and it is also sensitive to noise. To improve the quality of infrared image and visual effect, an adaptive weighted guided filter (AWGF) for infrared image enhancement algorithm was proposed. The core idea of AWGF algorithm is to propose an adaptive strategy to update the weights of guided filter (GF) parameters, which not only improves the accuracy of regularization parameter estimation in GF theory, but also achieves the purpose of removing infrared image noise and improving its detail contrast. A large number of real infrared images were used to verify AWGF algorithm, and good experimental results were obtained. Compared with other guided filtering algorithms, the halo phenomenon at the edge of infrared images processed by the AWGF algorithm is significantly avoided, and the evaluation parameter values of information entropy (IE), average gradient (AG), and moment of inertia (MI)are relatively high. This shows that the quality of infrared image processed by the AWGF algorithm is better.

关键词: infrared image, guided filter (GF), adaptive weight, image enhancement, regularization parameter

Abstract:

Key words: infrared image, guided filter (GF), adaptive weight, image enhancement, regularization parameter

参考文献

[1] CHEN J Y, YANG X M, LU L, et al. A novel infrared image enhancement based on correlation measurement of visible image for urban traffic surveillance systems. Journal of Intelligent Transportation Systems, 2020, 24(3): 290 -303.

[2] LIU K, TIAN Y Z. Research and analysis of deep learning image enhancement algorithm based on fractional differential. Chaos, Solitons and Fractals-Nonlinear Science, and Nonequilibrium and Complex Phenomena, 2020, 131, 109507: 1 -6.

[3] QIN Y C, LUO F G, LI M Z. A medical image enhancement method based on improved multi-scale Retinex algorithm. Journal of Medical Imaging and Health Informatics, 2020, 10(1): 152 - 157.

[4] RUDIN L I, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear

Phenomena, 1992, 60(1/2/3/4): 259 -268.

[5] CHARBONNIER P, BLANC-FERAUD L, AUBERT G, et al. Deterministic edge-preserving regularization in computed imaging. IEEE Transactions on Image Processing, 1997, 6(2): 298 -311.

[6] FARBMAN Z, FATTAL R, LISCHINSKI D, et al. Edge-preserving decompositions for multi-scale tone and detail

manipulation. ACM Transactions on Graphics, 2008, 27 (3): Artiale 67.

[7] TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images. Proceedings of the 6th International Conference on Computer Vision (ICCV’98), 1998, Jan 4 - 7, Bombay, India. Piscataway, NJ, USA: IEEE, 1998: 839 -846.

[8] LIU W, ZHANG P P, CHEN X G, et al. Embedding bilateral filter in least squares for efficient edge-preserving image

smoothing. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(1): 23 -35.

[9] PEI T Y, MA Q Y, XUE P F, et al. Nighttime haze removal using bilateral filtering and adaptive dark channel prior. Proceedings of the IEEE 4th International Conference on Image, Vision and Computing ( ICIVC’19 ), 2019, Jul 5 - 7, Xiamen, China. Piscataway, NJ, USA: IEEE, 2019: 218 -222.

[10] HE K M, SUN J, TANG X O. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397 -1409.

[11] LI Z G, ZHENG J H, ZHU Z J, et al. Weighted guided image filtering. IEEE Transactions on Image Processing, 2015, 24(1): 120 -129.

[12] KOU F, CHEN W H, WEN C Y, et al. Gradient domain guided image filtering. IEEE Transactions on Image Processing, 2015, 24(11): 4528 -4539.

[13] LONG P, LU H X. Weighted guided filtering algorithm using Laplacian-of-Gaussian edge detector. Journal of Computer Application, 2015, 35( 9): 2661 -2665 (in Chinese).

[14] KUMARI S A, KARUMURI R. Weighted guided image filtering for image enhancement. Proceedings of the 2nd International Conference on Communication and Electronics Systems ( ICCES’17 ), 2017, Oct 19 -20, Coimbatore, India.

Piscataway, NJ, USA: IEEE, 2017: 545 -548.

[15] DUAN P H, KANG X D, LI S T, et al. Fusion of multiple edge- preserving operations for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 10336 -10349.

[16] YUAN Y L, LUO X G. Weighted guided image filtering algorithm using phase congruency for image denoising. Journal of Xinyang Normal University: Natural Science edition, 2017, 30(3): 464 - 468 (in Chinese).

[17] MORRONE M C, OWENS R A. Feature detection from local energy. Pattern Recognition Letters, 1987, 6(5): 303 -313.

[18] KOVESI P. Phase congruency: a low-level image invariant. Psychological Research, 2000, 64(2): 136 -148.

[19] XIE W, YU J, TU Z G, et al. Fast algorithm for image defogging by eliminating halo effect and preserving details. Application Research of Computers, 2019, 36 ( 4 ): 1228 - 1231 ( in Chinese).

[20] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity. IEEE

Transactions on Image Processing, 2004, 13(4): 600 -612.

[21] RAHMAN Z, JOBSON D J, WOODELL G A. Multi-scale Retinex for color image enhancement. IEEE Transactions on Image Processing, 1996, 6(7): 1003 -1006.

[22] OTSU N. A threshold selection method from gray-level histograms. IEEE Transaction on Systems, Man and Cybernetics, 1979, 9(1): 62 -66.

[23] LIU Y, YU N M, FANG Y, et al. Low resolution cell image edge segmentation based on convolutional neural network. Proceedings of the IEEE 3rd International Conference on Image, Vision and Computing (ICIVC’18), 2018, Jun 27 -29, Chongqing, China. Piscataway, NJ, USA: IEEE, 2018: 321 -325.

Infrared image enhancement algorithm based on adaptive weighted guided filter

Infrared image enhancement algorithm based on adaptive weighted guided filter

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价