Computer Science ›› 2020, Vol. 47 ›› Issue (8): 241-244.doi: 10.11896/jsjkx.200300068

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Single Image Defogging Method Based on Weighted Near-InFrared Image Fusion

ZHU Zhen1, HUANG Rui2, ZANG Tie-gang3, LU Shi-jun4   

  1. 1 School of Information Technology, Guangdong Engineering Polytechnic College, Guangzhou 510520, China
    2 School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
    3 School of Mechatronics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    4 School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:ZHU Zhen, born in 1980, master, associa-te professor.Her main research intere-sts include algorithm design and analysis, image processing, database application, and software engineering.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61274085), Guangdong Provincial Science and Technology Department Project(2014A010103008, 2016B090918021), Guangdong Provincial Education Department Project(2018GKQNCX009) and Guangdong Provincial Education Department Project(2016gzpp031).

Abstract: The traditional image defogging method has the problem that the image contrast in the fog-free area is too high, which causes the generated image visual effect is unnatural in some cases.In order to obtain natural and clear defogging images, a new single image defogging method based on weighted near-infrared(NIR) image fusion is proposed.Image contrast is restored by fusing the detail components of NIR images into visible images of the same scene.The NIR image is weighted using the transmission map to prevent excessive enhancement in the fog-free region.The experimental results show that compared with the traditional single image defogging methods, the proposed method can effectively restore the image contrast, does not excessively strengthen the fog-free area, and has higher PSNR and SSIM values.

Key words: Image defogging, Near-infrared image, Weighting, Image fusion, PSNR, SSIM

CLC Number: 

  • TP391
[1] JIANG H Z, YOON S C, ZHUANG H.Predicting Color Traits of Intact Broiler Breast Fillets Using Visible and Near-Infrared Spectroscopy[J].Food Analytical Methods, 2017, 10(10):1-9.
[2] GU Y, YANG X, GAO Y.A Novel Total Generalized Variation Model for Image Dehazing[J].Journal of Mathematical Imaging and Vision, 2019, 61(6):1-13.
[3] XU L, HAN J, WANG T, et al.Global Image Dehazing via Frequency Perception Filtering[J].Journal of Circuits Systems & Computers, 2019, 28(9):1-11.
[4] NI W P, GAO X B, WANG Y.Single satellite image dehazing via linear intensity transformation and local property analysis[J].Neurocomputing, 2015, 175(6):25-39.
[5] HOU J, NING L, LING Y.Single image dehazing for visible remote sensing based on tagged haze thickness maps[J].Remote Sensing Letters, 2018, 9(7):156-165.
[6] HE K M, SUN J, FELLO W.Single Image Haze Removal UsingDark Channel Prior[J].IEEE Transactions on Pattern Analysis &Machine Intelligence, 2011, 33(12):2341-2353.
[7] CHENG D S, LIU H, ZHANG Y Q, et al.Single image defogging method combining adaptive dark channel prior and image fusion strategy [J].Journal of Harbin Institute of Technology, 2016, 48(11):35-40.
[8] STEPHAN J, NANKO V, WLADIMIR T.Near-infrared cut-off filters based on CMOS nanostructures for ambient light sensors and image sensors[J].Proceedings of SPIE-The International Society for Optical Engineering, 2014, 8994(9):2212-2212.
[9] ASHISH V V, VIKRAM M G.Visible and NIR image fusion using weight-map-guided Laplacian-Gaussian pyramid for improving scene visibility[J].Sadhana, 2017, 42(7):1-20.
[10] MIAO Q G, LI Y N.Research Status and Prospect of Image Dehazing Algorithm [J].Journal of Frontiers of Computer Science, 2017(11):7-14.
[11] LEX S, CL’EMENT F, SABINE S.Color image dehazing using the near-infrared∥2009 16th IEEE International Conference on Image Processing(ICIP).Cairo, 2009:1629-1632.
[12] ZHAO S N, WEI W B, PAN Z K, et al.Dehazing of a single color image based on dark primary color prior and MTV model [J].Computer Science, 2018, 45(3):274-276, 282.
[13] WEI Z L, XU G Y, ZHANG S X, et al.Dehazing Image Quality Evaluation System Based on MATLAB [J].Journal of Heilongjiang Institute of Technology(Comprehensive Edition), 2019, 19(8):36-39.
[14] LV X N, LIU Y Y, TAN Z, et al.A polarizing universal multiscale real-time image dehazing algorithm [J].Acta Photonica Sinica, 2019(8):111-121.
[15] WU D, ZHU Q S.The latest research progress of image defogging [J].Acta Automatica Sinica, 2015(2):221-239.
[16] ZHANG D Y, JU M Y, WANG X M.A Fast Image Dehazing Algorithm Based on Dark Channel Prior [J].Chinese Journal of Electronics, 2015, 43(7):1437-1443.
[17] LIANG J X, WAN X X, LIU Q.Research on the registrationmethod of visible light broadband spectral image based on SIFT algorithm [J].Journal of Hunan University of Technology, 2015(2):57-63.
[18] JIANG Y T, SUN C M, ZHAO Y.Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth[J].IEEE Transactions on Image Processing, 2017, 26(7):3397-3409.
[19] LONG J, SHI Z, TANG W, et al.Single Remote Sensing Image Dehazing[J].IEEE Geoence & Remote Sensing Letters, 2013, 11(1):59-63.
[20] CHENG D S, LIU H, ZHANG Y Q, et al.Single image defogging method combining adaptive dark channel prior and image fusion strategy [J].Journal of Harbin Institute of Technology, 2016, 48(11):36-40.
[21] SINGH D, KUMAR V, KAUR M.Single image dehazing using gradient channel prior[J].Applied Intelligence, 2019, 49(8):4276-4293.
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