利用上下边界估计的图像增强方法

doi:10.3969/j.issn.1001-2400.2015.01.024

Abstract

Abstract:

A novel image enhancement algorithm based on estimating the upper and lower boundary is presented. The method employs the upper and lower boundary to adjust the image details adaptively and dehaze the image, respectively and control the extent of image enhancement flexibly by adjusting the distance between the upper and lower boundary. The weighted median filter is adopted to calculate the upper and lower boundary for preserving image edges. Simulation results show that the proposed algorithm can improve the visual quality of the image efficiently, preserve the details, compress the high dynamic range, reduce halo artifacts and make the image more realistic and natural.

Key words: image enhancement, estimating the upper and lower boundary, Retinex theory

CLC Number:

TN919.81

ZHANG Ming;BAI Songhao. Image enhancement based on estimating the upper and lower boundary[J].J4, 2015, 42(1): 149-154.

References

［1］ Funt B, Ciurea F, McCann J. Retinex in Matlab［J］. Journal of Electronic Imaging, 2004,13(1): 48-57.
［2］ Jobson D J, Rahman Z U, Woodell G A. Properties and Performance of a Center/Surround Retinex［J］. IEEE Transactions on Image Processing, 1997, 6(3): 451-462.
［3］马时平, 张明, 毕笃彦, 等. 一种细节保持的Retinex图像增强算法［J］. 西安电子科技大学学报, 2010, 37(3): 541-546.
Ma Shiping, Zhang Ming, Bi Duyan, et al. Novel Detail Preserving Retinex Algorithm for Image Enhancement［J］. Journal of Xidian University, 2010, 37(3): 541-546.
［4］ Banic N, Loncaric S. Light Random Sprays Retinex: Exploiting the Noisy Illumination Estimation［J］. IEEE Signal Processing Letters, 2013, 20(12): 1240-1243.
［5］ He Kaiming, Sun Jian, Tang Xiaoou. Single Image Haze Removal Using Dark Channel Prior［C］//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 1956-1963.
［6］ Tarel J P, Hautibre N. Fast Visibility Restoration from a Single Color or Gray Level Image［C］//Proceedings of the 12th IEEE Intemational Conference on Computer Vision. Piscataway: IEEE, 2009: 2201-2208.
［7］ Tarel J P, Hautibre N, Caraffa L, et al. Vision Enhancement in Homogeneous and Heterogeneous Fog［J］. IEEE Intelligent Transportation Systems Magazine, 2012, 4(2): 6-20.
［8］ Cheng F C, Lin C H, Lin J L. Constant Time O(1) Image Fog Removal Using Lowest Level Channel［J］. Electronics Letters, 2012, 48(22): 1404-1406.
［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 Ronggui, Fu Jianfeng, Yang Zhixue, et al. A Novel Retinex Algorithm Based on Dark Channel Prior Model［J］. Acta Electronica Sinica, 2013, 41(6): 1188-1192.
［10］ He Kaiming, Sun Jian, Tang Xiaoou. Guided Image Filtering［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
［11］ Meylan L, Sosstrunk S. High Dynamic Range Image Rendering with a Retinex-based Adaptive Filter［J］. IEEE Transactions on Image Processing, 2006,15(9): 2820-2830.
［12］ Kim K, Bae J, Jaeseok K. Natural HDR Image Tone Mapping Based on Retinex［J］. IEEE Transactions on Consumer Electronics, 2011, 57(4): 1807-1814.

[1]	SUN Yanjing,WEI Li,ZHANG Nianlong,YUN Xiao,DONG Kaiwen,GE Min,CHENG Xiaozhou,HOU Xiaofeng. Person re-identification method combining the DD-GAN and Global feature in a coal mine [J]. Journal of Xidian University, 2021, 48(5): 201-211.
[2]	WANG Junxiang,HUANG Lin,ZHANG Ying,NI Jiangqun,LIN Lang. Algorithm for the detection of a low complexity contrast enhanced image source [J]. Journal of Xidian University, 2021, 48(1): 96-106.
[3]	LIU Zhiqiang,ZHANG Mingjin,CHI Yuan,LI Yunsong. Hardware Trojan detection algorithm based on deep learning [J]. Journal of Xidian University, 2019, 46(6): 37-45.
[4]	FENG Yuying;WANG Junping;MA Shuliang;LI Chao;WU Yao. New COPLIP model and its image enhancement algorithm [J]. Journal of Xidian University, 2018, 45(1): 66-71.
[5]	CUI Hua;ZHOU Tingjie;GUO Lu;YUAN Chao;SONG Huansheng. Image enhancement using the improved partial differential equation [J]. J4, 2014, 41(6): 195-198.
[6]	LI Jin;WANG Junping;WAN Guoting;LI Ziyang;XU Dan;CAO Honghua;ZHANG Guangyan. Novel algorithm for image enhancement with histogram equalization and MSRCR [J]. J4, 2014, 41(3): 103-109.
[7]	ZHANG Guangyan;WANG Junping;WANG Song;CAO Honghua;XU Dan;LI Jin;LI Changjiang. New image enhancement algorithm based on the SLIP model [J]. J4, 2014, 41(1): 105-109+169.
[8]	LI Quanhe;ZHA Yufei;XIONG Lei;BI Duyan. Novel method for haze degraded image scene rendition [J]. J4, 2013, 40(5): 99-106.
[9]	LI Quanhe;BI Duyan;HE Linyuan. Degradation process simulation model and its use in image enhancement [J]. J4, 2011, 38(6): 185-192.
[10]	MA Shi-ping;ZHANG Ming;BI Du-yan;XU Yue-lei. Novel detail preserving Retinex algorithm for image enhancement [J]. J4, 2010, 37(3): 541-546.
[11]	MA Miao;JIAO Li-li. Research on image enhancement for the heavy noised image based on structural similarity via gray relational analysis [J]. J4, 2010, 37(2): 346-352+365.
[12]	KOU Xiao-ming;LIU Shang-qian;HONG Ming;WANG Da-bao. Approach for adaptive infrared thermal image enhancement [J]. J4, 2009, 36(6): 1070-1074.
[13]	JIAN Yao-bo1;2;RUAN Shuang-chen2;QIN Han-lin3;ZHOU Hui-xin3. Edge Enhancement and Segmentation Algorithm based on the Snake Model and Lifting Scheme Wavelet Transform for the Infrared Target [J]. J4, 2007, 34(7): 136-139.
[14]	YANG Zhan-long;GUO Bao-long. A novel image mosaic method [J]. J4, 2007, 34(2): 249-253.
[15]	LIN Xiao-chun(1);LI Cun-zhi(2). Application of AMMDFM in underwater low-light-level image enhancement [J]. J4, 2006, 33(4): 543-546.

Image enhancement based on estimating the upper and lower boundary

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0