为了提高单幅图像去雾方法的准确性以及适用范围,提出一种MAGNet融合导向滤波的真实图像去雾方法。首先,根据雾在真实图像中分布特性以及成像原理,设计多注意力残差密集块,从而有效提取真实图像中与雾相关的特征并降低梯度消失风险;其次,构造基于所设计的多注意力残差密集块的端到端卷积神经网络,实现对有雾图像中雾的去除;最后,将导向滤波引入去雾问题中,实现对去雾后真实图像视觉效果的增强。实验结果表明：与已有代表性的图像去雾方法相比,该方法不仅能够对真实图像进行去雾,还可以对合成图像中的雾进行有效去除,且去雾效果更佳。
Abstract
In order to improve the accuracy and applicable scope of the single image dehazing method, a real-world image dehazing method combining multiple attention grid network(MAGNet) and guided filtering is proposed. Firstly, according to the distribution characteristics and imaging principles of haze in real-world images, the multi-attention residual dense block is designed to effectively extract haze-related features in real-world images and the risk of gradient disappearance is reduced. Secondly, the end-to-end convolutional neural network based on the designed multi-attention residual dense block is constructed to remove haze from hazy images. Finally, the guided filtering is introduced into the dehazing problem to enhance the visual effect of dehazing images. The experimental results show that compared with the existing representative image dehazing methods, the proposed method can not only remove haze from real-world images, but also effectively remove haze from synthetic images, and the visual effect of dehazing image is better.
关键词
计量学 /
MAGNet融合导向滤波 /
真实图像去雾 /
多注意力机制 /
图像处理 /
单幅图像去雾法
Key words
metrology /
MAGNet fusing guided filtering /
real-world image dehazing /
multi-attention mechanism /
image processing /
single image dehazing method
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参考文献
［1］陈辉, 杨剑, 黄晓铭, 等. 基于多视图立体视觉的沙堆三维尺寸测量研究 ［J］. 计量学报, 2019, 40(3): 403-408.
Chen H, Yang J, Huang X M, et al. Research on 3D Measurement of Sand Pile Based on Multi View Stereo Vision ［J］. Acta Metrologica Sinica, 2019, 40(3): 403-408.
［2］钟家栋, 孙斌, 赵玉晓, 等. 基于静力称重法的微小容量光学检测系统标定 ［J］. 计量学报, 2020, 41(8): 960-964.
Zhong J D, Sui B, Zhao Y X, et al. Calibration Method for Optical Measurement of Ultra-Micro Liquid Volume ［J］. Acta Metrologica Sinica, 2020, 41(8): 960-964.
［3］张立峰, 蒋玉虎. 电容层析成像三维图像重建研究 ［J］. 计量学报, 2019, 40(3): 462-465.
Zhang L F, Jiang Y H. Study of Three-dimensional Image Reconstruction for Electrical Capacitance Tomography ［J］. Acta Metrologica Sinica, 2019, 40(3): 462-465.
［4］Oakley J P, Bu H. Correction of simple contrast loss in color images ［J］. IEEE Transactions on Image Processing, 2007, 16(2): 511-522.
［5］Zhang J W, Li L, Zhang Y, et al. Video dehazing with spatial and temporal coherence ［J］. The Visual Computer, 2011, 27(6-8): 749-757.
［6］Fattal R. Single image dehazing ［J］. ACM Transactions on Graphics, 2008, 27(3): 1-9.
［7］Zhang H, Patel V M. Densely connected pyramid dehazing network ［C］//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 3194-3203.
［8］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.
［9］Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization ［J］. IEEE Transactions on Image Processing, 2000, 9(5): 889-896.
［10］Seow M J, Asari V K. Ratio rule and homomorphic filter for enhancement of digital colour image ［J］. Neurocomputing, 2006, 69(7-9): 954-958.
［11］Daubechies I. The wavelet transform, time-frequency localization and signal analysis ［J］. IEEE Transactions on Information Theory, 1990, 36(5): 961-1005.
［12］Jobson D J, Rahman Z, Woodell G A. Properties and performance of a center/surround retinex ［J］. IEEE Transactions on Image Processing, 1997, 6(3): 451-462.
［13］Jiang B, Woodell G A, Jobson D J. Novel multi-scale retinex with color restoration on graphics processing unit ［J］. Journal of Real-Time Image Processing, 2015, 10(2): 239-253.
［14］Tan R T. Visibility in bad weather from a single image ［C］//2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA, 2008: 1-8.
［15］He K M, Sun J, 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.
［16］Bui T M, Tran H N, Kim W, et al. Segmenting dark channel prior in single image dehazing ［J］. Electronics Letters, 2014, 50(7): 516-518.
［17］Yu T, Riaz I, Piao J, et al. Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior ［J］. IET Image Processing, 2015, 9(9): 725-734.
［18］Wang J B, He N, Zhang L L, et al. Single image dehazing with a physical model and dark channel prior ［J］. Neurocomputing, 2015, 149(2): 718-728.
［19］Zhu Q S, Mai J M, Shao L. A fast single image haze removal algorithm using color attenuation prior ［J］. IEEE Transactions on Image Processing, 2015, 24(11): 3522-3533.
［20］Cai B L, Xu X M, 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.
［21］Ren W Q, Liu S, Zhang H, et al. Single image dehazing via multi-scale convolutional neural networks ［C］//European Conference on Computer Vision. Amsterdam, Netherlands, 2016: 154-169.
［22］Li B Y, Peng X L, Wang Z Y, et al. AOD-Net: All-in-one dehazing network ［C］//2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017: 4780-4788.
［23］Ren W Q, Ma L, Zhang J W, et al. Gated fusion network for single image dehazing ［C］//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 3253-3261.
［24］Chen D D, He M M, Fan Q N, et al. Gated context aggregation network for image dehazing and deraining ［C］//2019 IEEE Winter Conference on Applications of Computer Vision. Waikoloa Village, USA, 2019: 1375-1383.
［25］Liu X H, Ma Y R, Shi Z H, et al. GridDehazeNet: Attention-based multi-scale network for image dehazing ［C］//2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea, 2019: 7313-7322.
［26］Dong H, Pan J S, Xiang L, et al. Multi-Scale Boosted Dehazing Network with Dense Feature Fusion ［C］//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA, 2020: 2154-2164.
［27］Qin X, Wang Z L, Bai Y B, et al. FFA-Net: Feature Fusion Attention Network for Single Image Dehazing ［C］//Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence. Hilton New York Midtown, New York, USA, 2020: 11908-11915.
［28］Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition ［C］//Proceedings of 3rd International Conference on Learning Representations. San Diego, USA, 2015: 1764-1776.
［29］Li B Y, Ren W Q, Fu D P, et al. Benchmarking single-image dehazing and beyond ［J］. IEEE Transactions on Image Processing, 2018, 28(1): 492-505.
基金
中央引导地方科技发展资金(216Z0301G);河北省自然科学基金(F2019203285)
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