一种基于反馈机制的闭环图像去雾算法

doi:10.11999/JEIT150494

Abstract
Figure/Table
References (23)
Related Citation (5)

Download: PDF (28849 KB)
Export: BibTeX | EndNote (RIS)

Abstract

To solve the problem of low adaptability in existing dehazing algorithms caused by the randomness and complexity of atmospheric environment, an adaptive closed-loop dehazing algorithm based on the feedback mechanism is proposed. Firstly, parameters in the proposed algorithm are initialized according to human visual system based characteristic cognitive assessment. Secondly, the estimation of dehazing strength is given as the feedback to correct parameters of local contrast adjustment method, and then adaptively improve the local contrast of image after removing additive light. Finally, the terminating condition is set according to the naturalness of image after dehazing to determine whether to output the result. Experimental results show that the proposed algorithm can adaptively improve the contrast of hazy images with a variety of degradation types and degrees, and the evaluation of information entropy and definition of dehazing results is better than those of other existing algorithms.

Key words： Image dehaze Visual mechanism Feedback mechanism

Received: 29 April 2015 Published: 04 December 2015

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61372167, 61379104)

Corresponding Authors: MA Shiping E-mail: mashiping@126.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	MA Shiping
	LI Quanhe
	ZHANG Shengchong

Cite this article:

MA Shiping,LI Quanhe,ZHANG Shengchong. An Adaptive Closed-loop Image Dehazing Algorithm Based on the Feedback Mechanism[J]. JEIT, 2016, 38(2): 400-407.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150494 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I2/400

[1]	KRISTOFOR B G and TRUONG Q N. An analysis and method for contrast enhancement turbulence mitigation[J]. IEEE Transactions on Image Processing, 2014, 23(7): 3179-3190.
[2]	WANG Y K and FAN C . Single image defogging by multiscale depth fusion[J]. IEEE Transactions on Image Processing, 2014, 23(11): 4826-4837.
[3]	SUN X M, SUN J X, ZHAO L R, et al. Improved single image haze removal using dark channel prior[J]. Journal of Image and Graphics, 2014, 19(3): 381-385.
[4]	SCHECHNER Y Y, NARASIMHAN S G, and NAYAR S K. Polarization-based vision through haze[J]. Appled Optics, 2003, 42(3): 511-525.
[5]	NARASIMHAN S G and NAYAR S K. Contrast restoration of weather degraded images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6): 713-724.
[6]	DING M and TONG R F. Efficient dark channel based image dehazing using quadtrees[J]. SCIENCE CHINA Information Sciences, 2013, 56(9): 231-239.
[7]	XIONG Y Q, YAN H, and YU C. Improved haze removal algorithm using dark channel prior[J]. Journal of Computational Information Systems, 2013, 9(14): 5743-5750.
[8]	庞春颖, 嵇晓强, 孙丽娜, 等. 一种改进的图像快速去雾新算法[J]. 光子学报, 2013, 42(7): 872-877.
	PANG Chunying, JI Xiaoqiang, SUN Lina, et al. An improved method of image fast defogging[J]. Acta Photonica Sinica, 2013, 42(7): 872-877.
[9]	GIBSON K B, VO D T, and NGUYEN T Q. An investigation of dehazing effects on image and video coding[J]. IEEE Transactions on Image Processing, 2012, 21(2): 662-673.
[10]	KIM J H, JANG W D, SIM J Y, et al. Optimized contrast enhancement for real-time image and video dehazing[J]. Journal of Visual Communication and Image Representation, 2013, 24(3): 410-425.
[11]	SHIAU Y H, YANG H Y, CHEN P Y, et al. Hardware implementation of a fast and efficient haze removal method [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(8): 1369-1374.
[12]	HE K M, SUN J, and TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.
[13]	BOSSU J, HAUTIERE N, and TAREL J P. Rain or snow detection in image sequences through use of a histogram of orientation of streaks[J]. International Journal of Computer Vision, 2011, 93(3): 348-367.
[14]	NISHINO K, KRATZ L, and LOMBARDI S. Bayesian defogging[J]. International Journal of Computer Vision, 2012, 98(4): 263-278.
[15]	方雯, 刘秉瀚. 反馈调节机制的暗通道去雾算法[J]. 计算机应用, 2013, 33(7): 1998-2001.
	FANG Wen and LIU Binghan. Dehazing algorithm based on dark channel with feedback regulation mechanism[J]. Journal of Computer Applications, 2013, 33(7): 1998-2001.
[16]	WALTHER D and KOCH C. Modeling attention to salient proto-objects[J]. Neural Networks, 2006, 19(9): 1395-1407.
[17]	王守觉, 丁兴号, 廖英豪, 等. 一种新的仿生彩色图像增强算法[J]. 电子学报, 2008, 36(10): 1970-1973.
	WANG Shoujue, DING Xinghao, LIAO Yinghao, et al. A novel bio-inspired algorithm for color image enhancement[J]. Acta Electronica Sinica, 2008, 36(10): 1970-1973.
[18]	PARIS S and DURAND F. A fast approximation of the bilateral filter using a signal processing approach[J]. International Journal of Computer Vision, 2009, 81(1): 24-52.
[19]	YANG Q X, TAN K H, and AHUJA N. Real-time O(1) bilateral filtering[C]. Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 557-564.
[20]	KIMMEL R, ELAD M, SHAKED D, et al. A variational framework for Retinex[J]. Computer Vision, 2003, 52(1): 7-23.
[21]	HE K M, SUN J, and TANG X O. Single image haze removal using dark channel prior[C]. Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 1956-1963.
[22]	陈季梦, 陈佳俊, 刘杰, 等. 基于结构相似度的大规模社交网络聚类算法[J]. 电子与信息学报, 2015, 37(2): 449-454. doi: 10.11999/JEIT140512.
	CHEN Jimeng, CHEN Jiajun, LIU Jie, et al. Clustering algorithm for large-scale social networks based on structural similarity[J]. Journal of Electronics & Information Technology, 2015, 37(2): 449-454. doi: 10.11999/JEIT140512.
[23]	薛模根, 朱虹, 袁广林. 基于在线判别式字典学习的鲁棒视觉跟踪[J]. 电子与信息学报, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325.
	XUE Mogen, ZHU Hong, and YUAN Guanglin. Robust visual tracking based on online discrimination dictionary learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325.