Infrared image enhancement method based on stationary wavelet transformation and Retinex

被引:0
|
作者
Zhan B. [1 ]
Wu Y. [1 ,2 ]
Ji S. [1 ]
机构
[1] School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing
来源
Guangxue Xuebao/Acta Optica Sinica | 2010年 / 30卷 / 10期
关键词
Fuzzy rules; Image enhancement; Infrared image processing; Multiscale Retinex method; Stationary wavelet transformation;
D O I
10.3788/AOS20103010.2788
中图分类号
学科分类号
摘要
As infrared image enhancement method based on wavelet transformation has the problem of unperfect visual effect, and Retinex enhancement algorithm can enhance visual effect of the image by improving brightness uniformity, a method based on stationary wavelet transformation and Retinex is proposed. Firstly, the infrared image is decomposed into high-frequency detail and low-frequency approximation components at various resolutions, and the low frequency subband image of the largest scale is enhanced by multiscale Retinex algorithm. Then, the high-frequency subband images are denoised by Bayesian shrinkage method, and the gain coefficients of high-frequency subbands are available by calculating the local contrast of the enhanced low-frequency subband based on fuzzy rules to get the enhanced high-frequency subband images. Finally, the enhanced image is reconstructed by the low-frequency subband and high-frequency subbands. Experiments with qualitative and quantitative evaluation are carried out for many images, and the proposed method is compared with histogram double equalization method, second generation wavelet transform method, curvelet transform method, and multiscale Retinex method. Experimental results show that the proposed method can enhance image details and suppress noise better, and the whole visual effect is improved significantly.
引用
收藏
页码:2788 / 2793
页数:5
相关论文
共 20 条
  • [1] Zhang F., Li C., Shi L., Detecting and tracking dim moving point target in IR image sequence, Infrared Physics & Technology, 46, 4, pp. 323-328, (2005)
  • [2] Qidwai U., Infrared image enhancement using H<sub>∞</sub> bounds for surveillance applications, IEEE Transactions on Image Processing, 17, 8, pp. 1274-1282, (2008)
  • [3] Chen Q., Bai L., Zhang B., Histogram double equalization in infrared image, Journal of Infrared and Millimeter Waves, 22, 6, pp. 428-430, (2003)
  • [4] Polesel A., Ramponi G., Mathews V.J., Image enhancement via adaptive unsharp masking, IEEE Transactions on Image Processing, 9, 3, pp. 505-510, (2000)
  • [5] Gong W., Wang Y., Contrast enhancement of infrared image via wavelet transform, Chinese Journal of National University of Defense Technology, 22, 6, pp. 117-119, (2000)
  • [6] Zhang C., Fu M., Jin M., Et al., Approach to enhancement contrast of infrared image based on wavelet transform, Journal of Infrared and Millimeter Waves, 23, 2, pp. 119-124, (2004)
  • [7] Qin H., Zhou H., Liu S., Et al., Nonlinear enhancement algorithm for infrared image based on second generation wavelet transform, Acta Optica Sinica, 29, 2, pp. 353-356, (2009)
  • [8] Starck J.-L., Murtagh F., Candes E.J., Et al., Gray and color image contrast enhancement by the curvelet transform, IEEE Transactions on Image Processing, 12, 6, pp. 706-717, (2003)
  • [9] Shi D., Li Q., Ni X., Et al., Infrared image nonlinear enhancement algorithm based on contourlet transform, Acta Optica Sinica, 29, 2, pp. 342-346, (2009)
  • [10] Jobson D.J., Rahman Z.-U., Woodell G.A., A multiscale retinex for bridging the gap between color images and the human observation of scenes, IEEE Transactions on Image Processing, 6, 7, pp. 965-976, (1997)