基于通道差值模型的导向滤波去雾算法及其FPGA实现

doi:10.16180/j.cnki.issn1007-7820.2023.08.001

Abstract

Abstract:

Computer vision systems are affected by foggy weather, resulting in poor quality images captured. To solve this problem, this study proposes a guided filtering dehazing algorithm based on channel difference model and its FPGA design. The channel difference model is obtained by separating the bright channel and dark channel of foggy image, and the model is used as a guide map for guided filtering to smooth the foggy image. Finally, a high boost filtering operation is performed to obtain a dehazed image. The hardware architecture is designed and implemented on FPGA. The experimental results show that the image scene after dehazing has uniform illumination, high degree of texture information recovery and high color fidelity. For an image of 480×270 size, the integrated frequency of the circuit is 108.448 MHz, the throughput is 323.47 MB·s^-1, and the time to complete the entire dehazing is 0.001 2 s. These results indicate that the proposed algorithm and its hardware design can effectively improve image visibility and dehazing speed.

Key words: dehazing, channel difference model, guided filtering, high boost filter, frequency, throughput, image processing, FPGA

CLC Number:

TP391.41

CAO Hongfang,WANG Xiaolei,DU Gaoming,LI Zhenmin,NI Wei. Design and FPGA Implementation of Dehazing Based on Channel Difference Model and Guided Filtering[J].Electronic Science and Technology, 2023, 36(8): 1-6.

Figures/Tables 10

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Table 1.

Quantitative comparison of dehazing methods in different scenarios"

图像	参数	文献[14]	文献[15]	本文
图6(a)	e	5.582 6	3.936 7	6.458 3
	$r -$	3.889 2	1.765 2	4.940 0
	σ	0.000 0	0.000 0	0.000 3
图6(b)	e	6.468 4	7.055 6	6.706 8
	$r -$	2.767 8	1.888 3	4.420 8
	σ	0.079 6	0.000 5	0.032 4
图6(c)	e	8.523 7	4.460 1	14.072 1
	$r -$	2.351 0	2.371 8	3.580 6
	σ	0.033 2	0.000 2	0.098 6
图6(d)	e	15.088 8	14.230 8	17.732 4
	$r -$	2.537 5	2.100 8	4.1210
	σ	0.003 7	0.000 0	0.020 2

Table 1.

Table 2.

Table 3.

Table 4.

References 18

[1]	董静薇, 赵春丽, 海博. 融合同态滤波和小波变换的图像去雾算法研究[J]. 哈尔滨理工大学学报, 2019, 24(1):66-77.
	Dong Jingwei, Zhao Chunli, Hai Bo. Research on image de-fog algorithm based on fusion homomorphic filtering and wavelet transform[J]. Journal of Harbin University of Science and Technology, 2019, 24(1):66-77.
[2]	董丽丽, 丁畅, 许文海. 基于直方图均衡化图像增强的两种改进方法[J]. 电子学报, 2018, 46(10):2367-2375. doi: 10.3969/j.issn.0372-2112.2018.10.009
	Dong Lili, Ding Chang, Xu Wenhai. Two improved methods based on histogram equalization for image enhancement[J]. Acta Electronica Sinca, 2018, 46(10):2367-2375. doi: 10.3969/j.issn.0372-2112.2018.10.009
[3]	Xu X G, Yang P, Liu Y L. Night image dehazing based on full-scale retinex algorithm[J]. Microelectronics & Computer, 2017, 34(7):132-136.
[4]	Cai B, Xu X, Jia K, et al. DehazeNet: An end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11):5187-5198. doi: 10.1109/TIP.2016.2598681 pmid: 28873058
[5]	何宜鸿, 李彦锋, 黄树恺, 等. 基于深度卷积神经网络的自适应图像去雾算法[J]. 电子科技, 2020, 33(8):70-73.
	He Yihong, Li Yanfeng, Huang Shukai, et al. Adaptive image dehazing algorithm based on deep convolutional neural network[J]. Electronic Science and Technology, 2020, 33(8):70-73.
[6]	Kim Y, Yim C. Image dehaze method using depth map estimation network based on atmospheric scattering model[C]. Barcelona: Proceedings of the International Conference on Electronics, Information, and Communication, 2020:1-3.
[7]	Choi H, Lim H, Yu S, et al. Haze removal of multispectral remote sensing imagery using atmospheric scattering model-based haze thickness map[C]. Seoul: Proceedings of the IEEE International Conference on Consumer Electronics-Asia, 2020:1-2.
[8]	He K, Sun J, Tang X. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(12):2341-2353. doi: 10.1109/TPAMI.2010.168
[9]	Vazquez-Corral J, Finlayson G D, Bertalmío M. Physical-based optimization for non-physical image dehazing methods[J]. Optics Express, 2020, 28(7):9327-9339. doi: 10.1364/OE.383799 pmid: 32225542
[10]	李喆, 李建增, 胡永江, 等. 混合先验与加权引导滤波的图像去雾算法[J]. 中国图象图形学报, 2019, 24(2):170-179.
	Li Zhe, Li Jianzeng, Hu Yongjiang, et al. Image dehazing algorithm based on hybrid prior and weighted guided filtering[J]. Journal of Image and Graphics, 2019, 24(2):170-179.
[11]	陆海俊, 汪荣贵, 杨娟, 等. 基于均值漂移的暗原色先验图像去雾算法[J]. 合肥工业大学学报(自然科学版), 2016, 39(9):1205-1210.
	Lu Haijun, Wang Ronggui, Yang Juan, et al. An image haze removal algorithm using dark prior based on mean shift[J]. Journal of Harbin University of Science and Technology, 2016, 39(9):170-179.
[12]	Shiau Y H, Kuo Y T, Chen P Y, et al. VLSI design of an efficient flicker-free video defogging method for real-time applications[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 29(1):238-251. doi: 10.1109/TCSVT.2017.2777140
[13]	Singh R, Biswas M. Contrast and color improvement based haze removal of underwater images using fusion technique[C]. Solan: Proceedings of the Fourth International Conference on Signal Processing, Computing and Control, 2017:138-143.
[14]	Du G, Wu J, Cao H, et al. A real-time effective fusion-based image defogging architecture on FPGA[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2021, 17(3):1-21.
[15]	Galdran A. Image dehazing by artificial multiple-exposure image fusion[J]. Signal Processing, 2018, 14(9):135-147.
[16]	Koga T, Yasuda A, Furukawa S, et al. A simplified and fast dehazing processing suitable for embedded systems[C]. Ishigaki: Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems, 2018:492-497.
[17]	Khurshid L, Raja G. Real time architecture for image dehazing[C]. Online: Proceedings of the Eleventh International Conference of Pattern Recognition Systems, 2021:79-84.
[18]	Zhao Z Y, Xiao A T, Guo J I. Video dehazing hardware accelerator design based on dark channel prior with sky preservation[C]. Hsinchu: Proceedings of the International Symposiumon VLSI Design, Automation and Test, 2020:1-4.

FPGA逻辑器件	设计利用总数
FPGA逻辑器件	已用	可用	占比/%
LUT总数	44 690	203 800	21.93
LUTRAM总数	33 288	64 000	52.01
触发器总数	11 450	407 600	2.81
DSP块总数	69	840	8.21
频率/MHz	-	108.448	-
时钟周期总数	-	130 351	-
吞吐量/MB·s^-1	-	323.47	-
时间/s	-	0.001 2	-

实现平台	计算时间/s	加速倍率
软件	0.440 0	1
硬件	0.001 2	367

图像大小	文献[16]/s	文献[17]/s	文献[18]/s	本文/s
640×480	0.025	0.064	0.013	0.003
1 024×768	0.058	0.150	0.031	0.007
1 280×960	0.092	0.235	0.048	0.011
1 920×1 080	0.158	0.407	0.084	0.019

Design and FPGA Implementation of Dehazing Based on Channel Difference Model and Guided Filtering

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