基于卷积神经网络的联合估计图像去雾算法

doi:10.13229/j.cnki.jdxbgxb20190443

摘要/Abstract

摘要：

室外拍摄的图像由于空气中的大气颗粒会具有较低的对比度和能见度，影响主观视觉和图像处理系统的有效性，为此本文提出了一种端到端的透射率和大气光联合估计去雾网络。通过共享特征模块获取透射率和大气光共有的全局特征，利用金字塔池化模块的多尺度卷积提取组合特征；然后，通过两个并行的分支分别估计透射率和大气光；最后，通过大气散射模型反演出无雾图像。实验结果表明:本文方法恢复图像较其他去雾方法的对比度更强，色彩更自然，网络优化参数更少。

关键词: 信息处理技术, 图像去雾, 卷积神经网络, 联合估计, 大气散射模型

Abstract:

Images captured in outdoor scenes typically have lower contrast and visibility due to atmospheric particles suspended in the air, directly affecting the effectiveness of subjective vision and intelligent image processing systems. To solve this problem, we propose an end-to-end network, which jointly estimates transmission map and atmospheric light effectively. The global characteristics of transmittance and atmospheric light are acquired simultaneously by shared feature module, and the multi-scale convolution of the pyramid pooling module is utilized to extract the combination feature. Then, the transmittance and atmospheric light are separately estimated by two parallel branch networks. Finally, the fog-free image is obtained by inversion of the atmospheric scattering model. The experiment results show that proposed method removes haze more effectively compared to these state-of-the-art methods, and the restored image has higher contrast and more natural color. Moreover, the proposed network has less parameters.

Key words: information processing technology, image dehazing, convolutional neural network, joint estimation, atmospheric scattering model

中图分类号:

TP751.1

王柯俨,王迪,赵熹,陈静怡,李云松. 基于卷积神经网络的联合估计图像去雾算法[J]. 吉林大学学报(工学版), 2020, 50(5): 1771-1777.

Ke-yan WANG,Di WANG,Xi ZHAO,Jing-yi CHEN,Yun-song LI. Image dehazing based on joint estimation via convolutional neural network[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1771-1777.

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参考文献 21

1	Wei Y, Yuan Q, Shen H, et al. A universal remote sensing image quality improvement method with deep learning[C]∥IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016: 6950-6953.
2	Zhang Q, Yuan Q, Zeng C, et al. Missing data reconstruction in remote sensing image with a unified spatial-temporal-spectral deep convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4274-4288.
3	Enomoto K, Sakurada K, Wang W, et al. Filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets[C]∥Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: HI, 2017: 1533-1541.
4	Kim T K, Paik J K, Kang B S. Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering[J]. IEEE Transactions on Consumer Electronics, 1998, 44(1): 82-87.
5	Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2000, 9(5): 889-896.
6	He K, Sun J, Tang X. Single image haze removal using dark channel prior[J]. IEEE Transactionson on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.
7	谭华春, 朱湧, 赵亚男, 等. 图像去雾的大气光幕修补改进算法[J]. 吉林大学学报：工学版, 2013, 43(): 389-393.
	Tan Hua-chun, Zhu Yong, Zhao Ya-nan, et al. Image fog removal using improved atmospheric veil inpainting[J]. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(Sup.1): 389-393.
8	王柯俨, 胡妍, 王怀, 等. 结合天空分割和超像素级暗通道的图像去雾算法[J]. 吉林大学学报：工学版, 2019, 49(4): 1377-1384.
	Wang Ke⁃yan, Hu Yan, Wang Huai, et al. Image dehazing algorithm by sky segmentation and superpixel⁃level dark channel[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1377-1384.
9	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.
10	Ren W, Liu S, Zhang H, et al. Single image dehazing via multi-scale convolutional neural networks[C]∥European Conference on Computer Vision, Springer, Cham, 2016: 154-169.
11	Yang H, Pan J, Yan Q, et al. Image dehazing using bilinear composition loss function[EB/OL]. [2019-04-30].
12	Li B, Peng X, Wang Z, et al. Aod-net: all-in-one dehazing network[C]∥IEEE International Conference on Computer Vision, Venice, 2017: 4770-4778.
13	McCartney E J. Optics of the atmosphere: scattering by molecules and particles[J]. New York: Wiley, 1976, 76: 23-32.
14	Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009: 248-255.
15	Everingham M, Gool L V, Williams C K I, et al. The pascal visual object classes(VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.
16	Liu F, Shen C, Lin G. Deep convolutional neural fields for depth estimation from a single image[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015: 5162-5170.
17	Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. [2019-04-30].
18	Li B, Ren W, Fu D, et al. Benchmarking single image dehazing and beyond[J]. IEEE Transactions on Image Processing, 2019, 28(1): 492-505.
19	Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
20	Saxena A, Sun M, Ng A Y. Make3d: learning 3d scene structure from a single still image[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(5): 824-840.
21	Silberman N, Hoiem D, Kohli P, et al. Indoor segmentation and support inference from RGBD images[C]∥European Conference on Computer Vision, Berlin, 2012: 746-760.

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Dataset	Metrics	Dehaze Net	MSCNN	Bilinear -Net	AOD-Net	Ours
Make3D	PSNR	20.01	22.64	16.21	19.37	23.15
Make3D	SSIM	0.87	0.94	0.59	0.87	0.94
NYU	PSNR	19.03	19.05	14.15	16.34	19.05
NYU	SSIM	0.86	0.66	0.86	0.80	0.87