全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

采用分裂Bregman的遥感影像亮度不均变分校正

DOI: 10.11834/jig.20140519

Keywords: 遥感影像,亮度不均校正,感知驱动,变分,分裂Bregman

Full-Text   Cite this paper   Add to My Lib

Abstract:

目的遥感影像成像过程由于受到传感器自身以及其他一些外部环境因素的影响,往往会呈现出整体的亮度不均,导致遥感影像解译和制图精度的降低。因此,需要对遥感影像进行亮度不均匀校正,提高影像的质量。方法感知驱动的亮度不均变分校正方法,是一种新型的单幅遥感影像亮度不均校正方法,它受人眼视觉系统特性的启发,能够在有效校正影像整体亮度的同时增强局部对比度。本文用分裂Bregman迭代实现了对感知驱动亮度不均变分校正模型的最优化求解,在实现对影像整体亮度不均校正的同时,大幅提高了计算效率。结果模拟实验和真实实验结果均表明,采用分裂Bregman的亮度不均变分校正模型需要较少的计算时间,从效率上比采用最速下降法的校正模型提高了约67倍。结论分裂Bregman方法能够有效求解感知驱动亮度不均变分模型,在保证校正结果整体亮度均匀,局部对比度增强的前提下,大大提高运算效率。

References

[1]  Wang M, Pan J. A new color balance method for large-scale seamless image database[J]. Remote Sensing for Land & Resources, 2006, 4: 10-13.[王密,潘俊. 面向无缝影像数据库应用的一种新的光学遥感影像色彩平衡方法[J]. 国土资源遥感, 2006, 4: 10-13.]
[2]  Li D R, Wang M, Pan J. Auto-dodging processing and its application for optical RS images[J]. Geomatics and Information Science of Wuhan University, 2006, 31(9): 753-756.[李德仁,王密,潘俊.光学遥感影像的自动匀光处理及应用[J]. 武汉大学学报: 信息科学版, 2006, 31(9): 753-756.]
[3]  Wang M, Pan J. A method of removing the uneven illumination for digital aerial image[J]. Journal of Image and Graphics, 2004, 9(6): 744-748.[王密,潘俊. 一种数字航空影像的匀光方法[J]. 中国图象图形学报, 2004, 9(6): 744-748.]
[4]  Wang X L, Huang L Q. Image restoration method based on improved local maximum entropy[J]. Journal of Image and Graphics, 2000, 5(7): 589-592.[王学良,黄廉卿. 改进的局部最大熵图象恢复方法[J]. 中国图象图形学报, 2000, 5(7): 589-592.]
[5]  Li H L, Shen H F,Du b, et al. A high-fidelity method of removing thin cloud from remote sensing digital images based on homomorphic filtering[J]. Remote Sensing Information, 2011, 1: 41-44,58.[李洪利,沈焕锋,杜博,等. 一种高保真同态滤波遥感影像薄云去除方法[J]. 遥感信息, 2011, 1: 41-44,58]
[6]  Zheng X D, Wang Y Q, Xu Z P, et al. Research on correction to uneven brightness of color image based on homomorphic filtering[J]. Microcomputer Information, 2009, 25(12-1): 114-116.[郑晓东,王永强,许增朴,等. 基于同态滤波彩色图像亮度不均校正方法[J]. 微计算机信息, 2009, 25(12-1): 114-116.]
[7]  Kimmel R, Elad M, Shaked D, et al. A variational framework for retinex[J]. International Journal of Computer Vision, 2003, 52(1): 7-23.
[8]  Li H F, Zhang L P, Shen H F. A perceptually inspired variational method for the uneven intensity correction of remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(8): 3053-3065.
[9]  Li H F, Shen H F, Zhang L P, et al. An uneven illumination correction method based on variational retinex for remote sensing image[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(6): 585-591.[李慧芳, 沈焕锋, 张良培, 等. 一种基于变分 Retinex 的遥感影像不均匀性校正方法[J]. 测绘学报, 2010, 39(6): 585-591.]
[10]  Goldstein T, Osher S. The split Bregman method for L1-regularized problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(2): 323-343.
[11]  Land E H, McCann J J. Lightness and retinex theory[J]. Journal of the Optical society of America, 1971, 61(1): 1-11.
[12]  Ng M K, Wang W. A total variation model for Retinex[J]. SIAM Journal on Imaging Sciences, 2011, 4(1): 345-365.
[13]  Li M. A fast algorithm for color image enhancement with total variation regularization[J]. Science China Information Sciences, 2010, 53(9): 1913-1916.
[14]  Yuan Q Q, Zhang L P, Shen H F. Hyperspectral image denoising employing a spectral-spatial adaptive total variation model[J]. IEEE Transactions on Geoscience and Remote Sensing 2012, 50(10): 3660-3677.
[15]  Zhang X, Burger M, Bresson X, et al. Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 253-276.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133