基于分层差分表达理论的图像视觉增强

耿爱辉; 万春明; 李毅; 张云峰; 曹立华; 冯强

doi:10.11999/JEIT161070

基于分层差分表达理论的图像视觉增强

doi: 10.11999/JEIT161070

2.
(长春理工大学　长春 130022) ②(中国科学院长春光学精密机械与物理研究所长春 130033)

基金项目:

国家自然科学基金(61205143)，吉林省科技厅重点项目(20110329)

计量
- 文章访问数: 1769
- HTML全文浏览量: 139
- PDF下载量: 315
- 被引次数: 0
出版历程
- 收稿日期: 2016-10-12
- 修回日期: 2017-02-15
- 刊出日期: 2017-04-19

Image Visual Enhancement Based on Layered Difference Representation

2.
(Changchun University of Science and Technplogy, Changchun 130022, China)

Funds:

The National Natural Science Foundation of China (61205143), The Science and Technology Department of Jilin Province Research Funding (20110329)

摘要

摘要: 视觉注意机制表明人眼趋向于关注感兴趣区域。为了实现图像视觉增强，该文提出基于2维直方图分层差分表达理论的图像增强方法。该算法首先检测图像的显著性区域，并对该区域进行分割，得到更适合人眼观测的显著图。然后统计原图中显著图对应区域的2维差分直方图，依据分层差分表达理论和考虑各层之间的内在联系，通过解线性优化问题得到显著性区域差分向量。定义原始差分向量代表原图特征，将两个向量加权相加后得到全局变换函数，重建得到视觉增强图像。实验结果表明：该方法有效地增强图像中人眼感兴趣区域对比度，提升细节信息。客观评价指标表明：与其他5种方法比较，该方法处理结果在3组实验中在保持全局亮度、提升峰值信噪比及人眼视觉系统敏感度信噪比指标优势明显。该方法增强后图像显著性区域的EME值适中，有利于视觉观测。客观指标与主观观察结果一致，表明该方法能有效改善图像视觉效果。
- 图像处理 /
- 人眼视觉增强 /
- 显著性检测 /
- 分层差分表达 /
- 2维直方图
Abstract: Human vision pays more attention to the interesting region than other areas. A method based on salient region detection for layered difference representation of 2D histogram is proposed to achieve visual enhancement. The algorithm detects the salient region by salient filtering and cuts salient region with a threshold for visual perception firstly. Then, 2D histogram is calculated for related region in original image of salient region, and statistical information in different layers is converted to layer 2 according to the inner relationship of each layer. Following a difference vector is gained though solving a constrained optimization problem of layered difference representation at a specified layer. To preserve the character of non-salient region, an origin difference vector is defined. Finally, output image is reconstructed by a transformation function, which is the result of two difference vectors for salient region and non-salient region. Experimental results show that the proposed method enhances contrast and details in salient region efficiently while protecting non-salient region in origin image. The objective evaluation parameters in three group experiments illustrate that the proposed algorithm can get better scores in protecting global mean lighting in non-salient region, increasing PSNR and HSNR of the whole image compared to other five algorithms. The EME value of images enhanced by the proposed method is moderate. The objective evaluation parameters are consistent with the subject observation, and it demonstrates the proposed method can achieve visual enhancement effectively.
- Image processing /
- Vision enhancement /
- Salient detection /
- Layered difference representation /
- 2D histogram

HTML全文

参考文献(25)

JI Tingting and WANG Guoyu. An approach to underwater image enhancement based on image structural decomposition [J]. Journal of Ocean University of China, 2015, 14(2): 255-260. doi: 10.1007/s11802-015-2426-2.

SUN Wei, GUO Baolong, LI Dajian, et al. Fast single image dehazing method for visible-light systems[J]. Optical Engineering, 2013, 52(9): 093103. doi: 10.1117/1.OE.52.9. 093103.

刘海波, 杨杰, 吴正平, 等. 改进的基于雾气理论的视频去雾[J]. 光学精密工程, 2016, 24(7): 1789-1798. doi: 10.3788/OPE. 20162407.1789.

LIU Haibo, YANG Jie, WU Zhengping, et al. Improved video defogging based on fog theory[J]. Optics and Precision Engineering, 2016, 24(7): 1789-1798. doi: 10.3788/OPE. 20162407.1789.

MOHAN Shelda and RAVISHANKAR M. Modified contrast limited adaptive histogram equalization based on local contrast enhancement for mammogram images[J]. Communications in Computer Information Science, 2013, 296: 397-403. doi: 10.1007/978-3-642-35864-7_60.

GAUTAM C and TIWARI N. Efficient color image contrast enhancement using range limited bi-histogram equalization with adaptive gamma correction[C]. 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, India, 2015: 175-180. doi: 10.1109/IIC.2015. 7150733.

卢彦飞, 张涛, 章程. 应用log-Gabor韦伯特征的图像质量评价[J]. 光学精密工程, 2015, 23(11): 3259-3269. doi: 10.3788/ OPE.20152311.3259.

LU Yanfei, ZHANG Tao, and ZHANG Cheng. Image quality assessment using log-Gabor Weber feature[J]. Optics and Precision Engineering, 2015, 23(11): 3259-3269. doi: 10.3788/ OPE.20152311.3259.

YANG Guocheng, LI Meiling, CHEN Leiting, et al. The nonsubsampled contourlet transform based statistical medical image fusion using generalized Gaussian density[J]. Computational Mathematical Methods in Medicine, 2015, 13(11): 1-13. doi: 10.1155/2015/262819.

DEMIREL H and ANBARIJAFARI FG. Discrete wavelet transform-based satellite image resolution enhancement[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 706-717. doi: 10.1109/TGRS.2010.2100401.

吴一全, 殷骏, 戴一冕. 基于人工蜂群优化的NSCT域图像模糊集增强方法[J]. 华南理工大学学报:自然科学版, 2015, 43(1): 59-65. doi: 10.3969/j.issn.1000-565X.2015.01.010.

WU Yiquan, YIN Jun, and DAI Yimian. Image enhancement in NSCT domain based on fuzzy sets and artificial bee colony optimization[J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(1): 59-65. doi: 10.3969/j.issn.1000-565X.2015.01.010.

张惊雷, 胡晓婷, 温显斌, 等. 基于Shearlet变换与区域分割的遥感图像融合[J]. 光电子激光, 2015, 26(12): 2393-2399. doi: 10.16136/j.joel.2015.12.0526.

ZHANG Jinglei, HU Xiaoting, WEN Xianbing, et al. Remote sensing image fusion algorithm based on Shearlet transform and region segmentation[J]. Journal of OptoelectronicsLaser, 2015, 26(12): 2393-2399. doi: 10.16136/j.joel.2015.12.0526.

LEE C L, LEE C, and KIM C S. Contrast enhancement based on layered difference representation of 2D histograms[J]. IEEE Transactions on Image Processing, 2013, 20(12): 5372-5384. doi: 10.1109/TIP.2013.2284059.

PERAZZI F, KRAHENBUHL P, and PRITCH Y. Salency filters: Contrast based filtering for salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 733-740. doi: 10.1109/CVPR.2012. 6247743.

KE W M, CHEN C R, and CHIU C T. BiTA/SWCE: Image enhancement with bilateral tone adjustment and saliency weighted contrast enhancement[J]. IEEE Transactions on Circuits and System for Video Technology, 2011, 21(3): 360-364. doi: 0.1109/TCSVT.2010.2087475.

LI Yi, ZHANG Yunfeng, CAO Lihua, et al. Visual enhancement based on salient region detection and layered difference representation[J]. Optical Review, 2016, 23(1): 1-7. doi: 10.1007/s10043-015-0163-9.

ZHANG X Y, CHAN K L, and CONSTABLE M. Atmospheric perspective effect enhancement of landscape photographs through depth-aware contrast manipulation[J]. IEEE Transactions on Multimedia, 2014, 16(3): 653-667. doi: 10.1109/TMM.2014.2299511.

OOI C H, KONG NSP, and IBRAHIM H. Bi-histogram equalization with a plateau limit for digital image enhancement[J]. IEEE Transactions on Consumer Electronics, 2009, 55(4): 2072-2080. doi: 10.1109/TCE.2009. 5373771.

AGAIAN S S, SILVER B, and PANETTA K A. Transform coefficient histogram-based image enhancement algorithms using contrast entropy[J]. IEEE Transactions on Image Processing, 2007, 16(3): 741-758. doi: 10.1109/TIP.2006.888338.

SIM K S, TSO C P, and TAN Y Y. Recursive sub-image histogram equalization applied to gray scale images[J]. Pattern Recognition Letters, 2007, 1209(28): 1209-1221. doi: 10.1016/j.patrec.2007.02.003.

安雪晶, 田媛. 无参考图像质量评价方法的设计原则[J]. 中国光学与应用光学, 2009, 2(2): 140-144. doi: 1674-2915(2009) 02-0140-05

AN Xuejing and TIAN Yuan. Design principle of no-reference image quality evaluation[J]. Chinese Journal of Optics and Applied Optics, 2009, 2(2): 140-144. doi: 1674-2915(2009)02- 0140-05.

USC SIPI图像库. ftp://sipi.usc.edu/pub/database/misc.zip 2015.12.

施引文献

资源附件(0)

访问统计