计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 500-504.doi: 10.11896/JsJkx.200100084
宋娅菲, 谌雨章, 沈君凤, 曾张帆
SONG Ya-fei, CHEN Yu-zhang, SHEN Jun-feng and ZENG Zhang-fan
摘要： 自然水体成像中湍流及悬浮颗粒等环境因素会造成水下采集的图像存在扭曲失真、分辨率低、背景模糊等问题,为了解决上述问题并进一步提高图像重建和复原的质量,提出了一种改进的基于残差网络的图像超分辨率重建方法,该方法将网络中的残差密集块和自适应机制进行融合,有效解决深度学习网络中经常遇到的梯度爆炸问题,同时能够抑制无用信息的学习,充分利用重要特征信息。为了使网络适应水下噪声环境,通过自建水下系统对目标板分别在清水中和浑浊微湍流水域中进行采集并对其进行图像配对生成训练对,并在河流和海洋水域下采集图像生成测试集。实验结果表明,在微湍流的海洋水域和河流水域中,较传统的水下图像处理和神经网络算法,使用改进的残差网络算法能够很好地对水下图像进行重建,重建图像的边缘信息得到了极大的保留,图像的重建效果更好。
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| [1] CHEN Y,YANG W,TAN H,et al.Image enhancement for LD based imaging in turbid water.Optik - International Journal for Light and Electron Optics,2016,127(2):517-521. [2] CHEN Y Z,YE T,CHENG C J,et al.Degradation and optimal recovery of underwater turbulent imaging.Optoelectronic Engineering,2018,45(12):55-65. [3] YANG A P,ZHENG J,WANG J,et al.Underwater image restoration based on color distortion removal and dark channel prio-ri.Journal of Electronics and Information Technology,2015,37(11):2541-2547. [4] HUO G Y,YAN S Y,WU W L,et al.Underwater image processing based on dark channel priors and adaptive color correction.Electrooptic & Control,2019,26(11):70-74. [5] SERIKAWA S,LU H.Underwater image dehazing using Joint trilateral filter.Computers & Electrical Engineering,2014,40(1):41-50. [6] LU H,LI Y,NAKASHIMA S,et al.Underwater image super-resolution by descattering and fusion.IEEE Access,2017,5:670-679. [7] NAKAGAWA Y,KIHARA K,TADOH R,et al.Super resolving of the depth map for 3D reconstruction of underwater terrain using Kinect//2016 IEEE 22nd International Confe-rence on Parallel and Distributed Systems (ICPADS).IEEE,2016:1237-1240. [8] ZHANG Q B,ZHANG X H,HAN H W.An underwater photoelectric image quality optimization method based on deep convolutional neural network .Acta Optica Sinica,2008,38(11):96-104. [9] XU Y,SUN M S.Underwater image enhancement method based on convolutional neural network .Journal of Jilin University (Engineering Ddition),2018,48(6):1895-1903. [10] DONG C,LOY C C,HE K,et al.Learning a deep convolutional network for image super-resolution//European cCnference on Computer Vision.Springer,Cham,2014:184-199. [11] SCHMIDHUBER J.Gradient Flow in Recurrent Nets:the Difficulty of Learning Long-Term Dependencies//Gradient Flow in Recurrent Nets:The Difficulty of Learning LongTerm Dependencies.Wiley-IEEE Press,2001. [12] HE K,ZHANG X,REN S,et al.Deep residual learning for ima-ge recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [13] KIM J,KWON LEE J,MU L K.Accurate image super-resolution using very deep convolutional networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1646-1654. [14] LIM B,SON S,KIM H,et al.Enhanced deep residual networks for single image super-resolution//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2017:136-144. [15] ZHANG Y,TIAN Y,KONG Y,et al.Residual dense network for image super-resolution//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:2472-2481. [16] HU J,SHEN L,SUN G.Squeeze-and-excitation networks//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141. [17] SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9. [18] WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity.IEEE Transactions on Image Processing,2004,13(4):600-612. [19] CHEN Y Z,YANG W L,TAN H Y,et al.Image enhancement for LD based imaging in turbid water.Optik - International [20] Journal for Light and Electron Optics,2016,127(2):517-521. [21] CHEN Y,YANG K.MAP-regularized robust reconstruction for underwater imaging detection.Optik-International Journal for Light and Electron Optics,2013,124(20):4514-4518. [22] ZHANG M,CHEN Y,PAN Y,et al.A Fast Image Deformity Correction Algorithm for Underwater Turbulent Image Distortion.Sensors,2019,19(18):3818. |
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