液晶与显示, 2016, 31 (1): 104, 网络出版: 2016-03-22   

尺度变化的Retinex红外图像增强

Infrared image enhancement method based on scale varies Retinex theory
作者单位
1 中国科学院 长春光学精密机械与物理研究所, 吉林 长春 130033
2 中国科学院大学, 北京 100049
摘要
经典Retinex模型增强算法采用固定尺度高斯核平滑滤波, 导致单一尺度Retinex无法进行全局有效增强, 而多尺度Retinex权重系数选取困难, 二者均不能满足视觉要求。针对以上问题, 基于人眼视觉掩盖效应提出一种尺度变化高斯核平滑滤波的Retinex算法。首先利用人眼视觉掩盖效应的屏蔽函数检测像素邻域空间细节, 依据像素区域细节信息丰富程度设计出尺度变化的高斯平滑滤波器, 实现照度估计, 最后对尺度变化高斯平滑滤波器实现提出实用方法。实验证明本文算法有效提高红外图像对比度, 增强细节信息, 在主观视觉效果和客观评价指标上整体优于修正对比度限制直方图均衡算法、单尺度Retinex、多尺度Retinex及平稳小波和Retinex增强算法。
Abstract
Classic Retinex algorithm, applied in image enhancement with fixed Gaussian filter, couldnt meet demand of human vision. The result of Single Scale Retinex algorithm was unable to satisfy effective enhancement of the whole image, and it was difficult to use Multi-scale Retinex algorithm estimating illumination correctly without suitable coefficient. As a result, a novel scale varies algorithm of Retinex theory based on human visual masking effects was proposed. The results of human visual masking function, which can estimate how much details exist in the pixel neighborhood efficiently, was used to design the scale of Gaussian filter. The new algorithm was designed to get the correct illumination of a infrared image. At last, a practical solution was presented to cut down time consuming .Experimental results show that the proposed algorithm improves the contrast and enhance the details of infrared images. Moreover, the proposed algorithm is better than histogram equalization, Modified Contrast Limited Adaptive Histogram Equalization, single scale Retinex, Multi-scale Retinex and Stationary Wavelet and Retinex in subjective visual effects and objective evaluation parameters.
参考文献

[1] 于天河, 戴景民.基于多重分形的红外图像增强技术[J].红外与激光工程, 2010, 39(1): 184-188.

    YU T H, DAI J M. Multifractal theory based infrared image enhancement technology[J]. Infrared and Laser Engineering, 2010, 39(1): 184-188. (in Chinese)

[2] 王炳健, 刘上乾, 周慧鑫, 等.基于平台直方图的红外图像自适应增强算法[J].光子学报, 2005, 34(2): 299-301.

    WANG B J, LIU S Q, ZHOU H X, et al. Self-Adaptive contrast enhancement algorithm for infrared images based on plateau histogram[J]. Acta Photonica Sinica, 2005, 34(2): 299-301. (in Chinese)

[3] 占必超, 吴一全, 纪守新.基于平稳小波变换和Retinex的红外图像增强方法[J].光学学报, 2010, 30(10): 2788-2793.

    ZHAN B C, WU Y Q, JI S X. Infrared image enhancement method based on stationary wavelet transformation and retinex[J]. Acta Optica Sinica, 2010, 30(10): 2788-2793. (in Chinese)

[4] 曹军峰, 史家成, 罗海波, 等.采用聚类分割和直方图均衡的图像增强算法[J].红外与激光工程, 2012, 41(12): 3436-3441.

    CAO J F, SHI J C, LUO H B, et al. Image enhancement using clustering and histogram equalization[J]. Infrared and Laser Engineering, 2012, 41(12): 3436-3441. (in Chinese)

[5] STARCK J L, MURTAGH F, CANDES E J, et al. Gray and color image contrast enhancement by the cuevelet transform[J]. IEEE Transactions on Image Processing, 2003, 12(6): 706-717.

[6] 石丹, 李庆武, 倪雪, 等.基于Contourlet变换的红外图像非线性增强算法[J].光学学报, 2009, 29(2): 342-346.

    SHI D, LI Q W, NI X, et al. Infrared image nonlinear enhancement algorithm based on contourlet transform[J]. Acta Optica Sinica, 2009, 29(2): 342-346. (in Chinese)

[7] LAND E H . The Retinex theory of color vision[J]. Scientific American, 1977, 237(6): 108-129.

[8] JOBSON D J, RAHMAN Z U, WOODELL G A. A multi scale Retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing, 1997, 6(7): 965-976.

[9] 汪荣贵, 傅剑峰, 杨志学, 等.基于暗原色先验模型的Retinex算法[J].电子学报, 2013, 41(6): 1188-1192.

    WANG R G, FU J F, YANG Z X, et al. A novel Retinex algorithm based on dark channel prior model[J]. Acta Electronica Sinica, 2013, 41(6): 1188-1192. (in Chinese)

[10] JANG J H, BAE Y, RA J B. Contrast-enhanced fusion of multisensor images using subband-decomposed multiscale Retinex[J]. IEEE Transactions on Image Processing, 2012, 21(8): 3479-3490.

[11] JANG J H, KIM S D, RA J B. Enhancement of optical remote sensing images by subband-decomposed multiscale Retinex with hybrid intensity transfer function[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(5): 983-987.

[12] ANDERSON G L, NETRAVALI A N. Image restoration based on a subjective criterion[J]. IEEE Transactions on Systems, Man and Cybernetics, 1976, SMC-6(12): 845-853.

[13] KATSAGGELOS A K, BIEMOND J, SCHAFER R W, et al. A regularized iterative image restoration algorithm[J]. IEEE Transactions on Signal Processing, 1991, 39(4): 914-929.

[14] 余庆军, 谢胜利.基于人类视觉系统的各向异性扩散图像平滑方法[J].电子学报, 2004, 32(1): 17-20.

    YU Q J, XIE S L. An anisotropic diffusion image smoothing method based on human visual system[J]. Acta Electronica Sinica, 2004, 32(1): 17-20. (in Chinese)

[15] 邱望仁, 刘晓东.基于FCM的广义模糊时间序列模型[J].模糊系统与数学, 2013, 27(6): 111-117.

    QIU W R, LIU X D. A novel generalized fuzzy time series forecasting model based on FCM[J]. Fuzzy Systems and Mathematics, 2013, 27(6): 111-117. (in Chinese)

[16] MOHAN S, RAVISHANKAR M. Modified contrast limited adaptive histogram equalization based on local contrast enhancement for mammogram images[C]. Proceedings of the 2nd International Joint Conference, Berlin Heidelberg, Germany: Springer, 2013, 296: 397-403.

李毅, 张云峰, 年轮, 崔爽, 陈娟. 尺度变化的Retinex红外图像增强[J]. 液晶与显示, 2016, 31(1): 104. LI Yi, ZHANG Yun-feng, NIAN Lun, CUI Shuang, CHEN Juan. Infrared image enhancement method based on scale varies Retinex theory[J]. Chinese Journal of Liquid Crystals and Displays, 2016, 31(1): 104.

本文已被 3 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!