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Adaptive dodging method based on variational mask for remote sensing images

  • role： First author第一作者
  • Affiliation：

    Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

  • Email：chxyls@163.com
  • Introduction：李烁(1990—　)，男，博士研究生，研究方向为数字摄影测量和数字图像处理。E-mail：chxyls@163.com
Shuo LI,  
  • Affiliation：

    Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

Hui WANG,  
  • Affiliation：

    Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

Liyong WANG,  
  • Affiliation：

    Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

Xiangzhou YU,  
  • Affiliation：

    Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China

Le YANG

реферат

The illumination of a captured remote sensing image may be unevenly distributed because of the influences of sensors and external factors, such as light and fog. Uneven illumination degrades the quality of an image and causes interference in the subsequent processing (e.g., image interpretation, image classification, and image mosaicking). The process of correcting uneven illumination is called image dodging, which is of great practical significance and application value. Current methods focus on how to obtain the illumination distribution within an image and compensate for the different parts of the image in different degrees. The mask dodging method is widely used for remote sensing images. The background is generated by a low-pass filter to simulate the illumination distribution and subtracted from the original image to obtain the ideal image with even illumination. Estimation of the background image is a key step in the mask dodging method and directly affects the resulting image, which depends on the low-pass filter and its dimension parameter. Automatic selection of the dimension parameter is a problem in the mask dodging method. However, no suitable solution to the problem exists, and manual adjustment is usually required in practical applications. An adaptive dodging method based on a variational mask is proposed in this study to resolve the poor universality of the mask dodging method. A variational energy function is constructed according to the prior information of the image itself and the mathematical model of the mask dodging method. L1 and L2 norms are adopted to constrain the texture and detail of the ideal image and the smoothness of the background image, respectively. Estimation of the background image is transformed into an optimal solution of the variational energy function. Solving the problem directly is often difficult because the variational function contains two norms. The split Bregman method is used to decompose the variational function into three subproblems, which are solved by alternate iteration, to simplify the calculation. Optimal background and ideal images are then obtained simultaneously. Two groups of experiments are conducted on three remote sensing images to verify the effectiveness of the proposed method. Compared with mask dodging methods with three different dimension parameters, the proposed method performs better in visual evaluation and quantitative measurements. The proposed method can effectively eliminate uneven illumination while maintaining the textures and details of the remote sensing images.

ключеви́че слова́

image dodging;variational Mask;uneven illumination;background image;split Bregman

References

  1. 1.
    Chen C and Xu G L. 2016. A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography. Computers and Mathematics with Applications, 71(8): 1537–1559
  2. 2.
    Fan C N and Zhang F Y. 2011. Homomorphic filtering based illumination normalization method for face recognition. Pattern Recognition Letters, 32(10): 1468–1479
  3. 3.
    Guo C Z, Shi W J, Qin Z Y and Geng Z X. 2016. Non-convex sparsity regularization for wave back restoration of space object images. Optics and Precision Engineering, 24(4): 902–912 (
  4. 4.
    Huo L J, He B and Zhou D B. 2017. A destriping method with multi-scale variational model for remote sensing image. Optics and Precision Engineering, 25(1): 198–207 (
  5. 5.
    Jiang B, Woodell G A and Jobson D J. 2015. Novel multi-scale Retinex with color restoration on graphics processing unit. Journal of Real-Time Image Processing, 10(2): 239–253
  6. 6.
    Kimmel R, Elad M, Shaked D, Keshet R and Sobel I. 2003. A variational framework for retinex. International Journal of Computer Vision, 52(1): 7–23
  7. 7.
    Li D R, Wang M and Pan J. 2006. Auto-dodging processing and its application for optical RS images. Geomatics and Information Science of Wuhan University, 31(9): 753–756 (
  8. 8.
    Li H F, Shen H F, Zhang L P and Li P X. 2010. An uneven illumination correction method based on variational Retinex for remote sensing image. Acta Geodaetica et Cartographica Sinica, 39(6): 585–591 (
  9. 9.
    Sun M W. 2009. Research on key technology of automatic and fast DOM generation. Wuhan: Wuhan University
  10. 10.
    Tang S, Gong W G and Zhong J H. 2013. Sparsity and smoothing multi-regularization constraints for blind image deblurring. Journal of Software, 24(5): 1143–1154 (
  11. 11.
    Wang M and Pan J. 2004. A method of removing the uneven illumination for digital aerial image. Journal of Image and Graphics, 9(6): 744–748 (
  12. 12.
    Yao F, Wan Y C and Hu H. 2013. Research on the improved image dodging algorithm based on Mask technique. Remote Sensing Information, 28(3): 8–13 (
  13. 13.
    Yuan X X, Han Y T and Fang Y. 2014. Improved Mask dodging algorithm for aerial imagery. Journal of Remote Sensing, 18(3): 630–641 (
  14. 14.
    Zhang Z, Zhu B S and Zhu S L. 2010. An improved MASK dodging method based on contrast consistency processing. Journal of Geomatics Science and Technology, 27(1): 54–56 (
  15. 15.
    Zhang Z, Zhu B S, Zhu S L and Cao W. 2009. Improved MASK dodging method based on wavelet. Journal of Remote Sensing, 13(6): 1074–1081 (
  16. 16.
    Zhang Z X, Li Z J, Zhang J Q and Zheng L. 2003. Use discrete chromatic space to tune the image tone in a color image mosaic//Proceedings of the Third International Symposium on Multispectral Image Processing and Pattern Recognition. Beijing: SPIE [DOI: 10.1117/12.538550]
  17. 17.
    Zhang Z X and Zou S B. 2011. An improved algorithm of Mask image dodging for aerial image// Proceedings of MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems and Other Applications. Guilin: SPIE [DOI: 10.1117/12.901869]
  18. 18.
    Zheng J, Xu C G, Xiao D G, Li H and Huang H. 2003. The technique of digit image’s illumination uneven elimination. Transactions of Beijing Institute of Technology, 23(3): 285–289 (
  19. 19.
    Zhu S L, Zhang Z, Zhu B S and Cao W. 2011. Experimental comparison among five algorithms of brightness and contrast homogenization. Journal of Remote Sensing, 15(1): 111–122 (

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