徐浩,谭一博,刘博文,王国宇

• 1:中共海洋大学信息科学与工程学院

摘要(Abstract)：

基于暗通道先验的图像去雾算法因其简单有效而得到广泛应用。经典算法中由于透射率图估计存在块效应而需要对透射率图进行滤波细化。本文提出了一种基于超像素分割和暗通道先验的透射率估计方法,用超像素分割获得的不规则区域替代经典算法中的固定窗口,从而避免了透射率图估计的块效应问题,并采用松弛策略以获得鲁棒性和准确性更好的透射率结果,无需进行另外的滤波细化步骤即可完成图像去雾复原。实验结果证实了本文所提方法的有效性。

关键词(KeyWords)： 图像去雾;超像素;暗通道;透射率

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(61571407)资助~~

作者(Author): 徐浩,谭一博,刘博文,王国宇

DOI: 10.16441/j.cnki.hdxb.20190333

参考文献(References)：

• [1] Qin M,Xie F,Li W,et al.Dehazing for multispectral remote sensing images based on a convolutional neural network with the residual architecture[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(5):1645-1655.
• [2] Li C,Guo J,Porikli F,et al.A cascaded convolutional neural network for single image dehazing[J].IEEE Access,2018,6:24877-24887.
• [3] Song Y,Li J,Wang X,et al.Single image dehazing using ranking convolutional Neural network[J].IEEE Transactions on Multimedia,2018,20(6):1548-1560.
• [4] Narasimhan S G,Nayar S K.Vision and the atmosphere[J].International Journal of Computer Vision,2002,48(3):233-254.
• [5] Treibitz T,Schechner Y Y.active polarization descattering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(3),385-399.
• [6] Fattal R.Single image dehazing[J].ACM Transactions on Graphics,2008,27(3):1-9.
• [7] Fattal R.Dehazing using Color-Lines[J].ACM Transactions on Graphics,2014,34 (1):1-13.
• [8] Tan R.Visibility in Bad Weather from a Single Image[C].[s.1.]:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2008),2008:1-8.
• [9] Tarel J P.Fast visibility Restoration from a Single Color or Gray Level Image[C].[s.1.]:Proceedings of IEEE Conference on International Conference on Computer Vision (ICCV’2009),2009,10:20-28.
• [10] He K,Sun J,Tang X.Single image haze removal using dark channel prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(12):2341-2353.
• [11] He K,Sun J,Tang X.Guided image filtering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409.
• [12] Ren X,Malik J.Learning a Classification Model for Segmentation[C].[s.1.]:Proceedings of IEEE International Conference on Computer Vision (ICCV’2003),2003,1:10-17.
• [13] 吴晟杰,基于超像素聚类的图像分割算法研究[D].西安:西安电子科技大学,2014.Wu Shengjie.Research of Image Segmentation Algorithm Based on Superpixels Clustering[D].Xi′an:Xidian University,2014.
• [14] 宋熙煜,基于超像素的图像分割技术研究[D].郑州:解放军信息工程大学,2015.Song Xiyu.Research on Image Segmentation Methods Based on Superpixel[D].Zhengzhou:PLA Information Engineering University,2015.
• [15] 周宝.基于超像素聚类的图像分割方法研究[D].南京:东南大学,2015.Zhou bao.Image Segmentation Using SLIC Superpixels and Affinity Propagation Clustering[D].Nanjing:Southeast University,2015.
• [16] Shi J B,Malik J.Normalized Cuts and Image Segmentation[C].[s.1.]:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’97),1997:731-737.
• [17] Moore A P,Prince S,Warrell J,et al.Superpixel lattices[C].[s.1.]:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR’2008),2008:1-8.
• [18] Comaniciu D,Meer P.A robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
• [19] Levinshtein A,Stere A,Kutulakos K N,et al.Turbopixels:fast superpixels using geometric flows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(12):2290-2297.
• [20] Achanta R,Shaji A,Smith K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
• [21] Hautiere N,Tarel J P,Aubert D,et al.Blind contrast enhancement assessment by gradient ratioing at visible edges[J].Image Analysis & Stereology Journal,2008,27(2):87-95.

文章评论(Comment)：